Amazon Web Services MLS-C01 AWS Certified Machine Learning - Specialty Exam Practice Test

Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting. References:

Principal Component Analysis (PCA) Algorithm - Amazon SageMaker

Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog

Machine Learning- Prinicipal Component Analysis | i2tutorials

 A precision-recall curve is a plot that shows the trade-off between the precision and recall of a binary classifier as the decision threshold is varied. It is a useful tool for evaluating and comparing the performance of different models. To generate a precision-recall curve, the following steps are needed:

Calculate the precision and recall values for different threshold values using the predictions and the true labels of the data.

Plot the precision values on the y-axis and the recall values on the x-axis for each threshold value.

Optionally, calculate the area under the curve (AUC) as a summary metric of the model performance.

Among the four options, option C requires the least coding effort to generate and share a visualization of the daily precision-recall curve from the predictions. This option involves the following steps:

Run a daily Amazon EMR workflow to generate precision-recall data: Amazon EMR is a service that allows running big data frameworks, such as Apache Spark, on a managed cluster of EC2 instances. Amazon EMR can handle large-scale data processing and analysis, such as calculating the precision and recall values for different threshold values from 100 TB of predictions. Amazon EMR supports various languages, such as Python, Scala, and R, for writing the code to perform the calculations. Amazon EMR also supports scheduling workflows using Apache Airflow or AWS Step Functions, which can automate the daily execution of the code.

Save the results in Amazon S3: Amazon S3 is a service that provides scalable, durable, and secure object storage. Amazon S3 can store the precision-recall data generated by Amazon EMR in a cost-effective and accessible way. Amazon S3 supports various data formats, such as CSV, JSON, or Parquet, for storing the data. Amazon S3 also integrates with other AWS services, such as Amazon QuickSight, for further processing and visualization of the data.

Visualize the arrays in Amazon QuickSight: Amazon QuickSight is a service that provides fast, easy-to-use, and interactive business intelligence and data visualization. Amazon QuickSight can connect to Amazon S3 as a data source and import the precision-recall data into a dataset. Amazon QuickSight can then create a line chart to plot the precision-recall curve from the dataset. Amazon QuickSight also supports calculating the AUC and adding it as an annotation to the chart.

Publish them in a dashboard shared with the Business team: Amazon QuickSight allows creating and publishing dashboards that contain one or more visualizations from the datasets. Amazon QuickSight also allows sharing the dashboards with other users or groups within the same AWS account or across different AWS accounts. The Business team can access the dashboard with read-only permissions and view the daily precision-recall curve from the predictions.

The other options require more coding effort than option C for the following reasons:

Option A: This option requires writing code to plot the precision-recall curve from the data stored in Amazon S3, as well as creating a mechanism to share the plot with the Business team. This can involve using additional libraries or tools, such as matplotlib, seaborn, or plotly, for creating the plot, and using email, web, or cloud services, such as AWS Lambda or Amazon SNS, for sharing the plot.

Option B: This option requires transforming the predictions into a format that Amazon QuickSight can recognize and import as a data source, such as CSV, JSON, or Parquet. This can involve writing code to process and convert the predictions, as well as uploading them to a storage service, such as Amazon S3 or Amazon Redshift, that Amazon QuickSight can connect to.

Option D: This option requires writing code to generate precision-recall data in Amazon ES, as well as creating a dashboard to visualize the data. Amazon ES is a service that provides a fully managed Elasticsearch cluster, which is mainly used for search and analytics purposes. Amazon ES is not designed for generating precision-recall data, and it requires using a specific data format, such as JSON, for storing the data. Amazon ES also requires using a tool, such as Kibana, for creating and sharing the dashboard, which can involve additional configuration and customization steps.

Precision-Recall

What Is Amazon EMR?

What Is Amazon S3?

[What Is Amazon QuickSight?]

[What Is Amazon Elasticsearch Service?]

 To use a custom algorithm in Amazon SageMaker, the Docker container image must have an executable file named train that acts as the ENTRYPOINT for the container. This file is responsible for running the training code and communicating with the Amazon SageMaker service. The train file must be in the PATH of the container and have execute permissions. The other options are not valid ways to package the Docker container for Amazon SageMaker. References:

Use Docker containers to build models - Amazon SageMaker

Create a container with your own algorithms and models - Amazon SageMaker

The metrics that will indicate an ML solution that will provide the greatest probability of detecting an abnormality are precision and recall. Precision is the ratio of true positives (TP) to the total number of predicted positives (TP + FP), where FP is false positives. Recall is the ratio of true positives (TP) to the total number of actual positives (TP + FN), where FN is false negatives. A high precision means that the ML solution has a low rate of false alarms, while a high recall means that the ML solution has a high rate of true detections. For the chemical company, the goal is to avoid process abnormalities, which are marked as 1 in the labels. Therefore, the company needs an ML solution that has a high recall for the positive class, meaning that it can detect most of the abnormalities and minimize the false negatives. Among the four options, option B has the highest recall for the positive class, which is 0.98. This means that the ML solution can detect 98% of the abnormalities and miss only 2%. Option B also has a reasonable precision for the positive class, which is 0.61. This means that the ML solution has a false alarm rate of 39%, which may be acceptable for the company, depending on the cost and benefit analysis. The other options have lower recall for the positive class, which means that they have higher false negative rates, which can be more detrimental for the company than false positive rates.

1: AWS Certified Machine Learning - Specialty Exam Guide

2: AWS Training - Machine Learning on AWS

3: AWS Whitepaper - An Overview of Machine Learning on AWS

4: Precision and recall

The solution A will meet the requirements with the least development effort because it uses Amazon Kendra, which is a highly accurate and easy to use intelligent search service powered by machine learning. Amazon Kendra can index company documents from various sources and formats, such as PDF, HTML, Word, and more. Amazon Kendra can also integrate with chatbots by using the Amazon Kendra Query API operation, which can understand natural language questions and provide relevant answers from the indexed documents. Amazon Kendra can also provide additional information, such as document excerpts, links, and FAQs, to enhance the chatbot experience1.

The other options are not suitable because:

Option B: Training a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents, deploying the model as a real-time Amazon SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BiDAF network, which is a complex deep learning model for question answering. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic2.

Option C: Training an Amazon SageMaker BlazingText model based on past customer questions and company documents, deploying the model as a real-time SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BlazingText model, which is a fast and scalable text classification and word embedding algorithm. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic3.

Option D: Indexing company documents by using Amazon OpenSearch Service and integrating the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation will not meet the requirements effectively. Amazon OpenSearch Service is a fully managed service that provides fast and scalable search and analytics capabilities. However, it is not designed for natural language question answering, and it may not provide accurate or relevant answers for the chatbot. Moreover, the k-NN Query API operation is used to find the most similar documents or vectors based on a distance function, not to find the best answers based on a natural language query4.

1: Amazon Kendra

2: Bidirectional Attention Flow for Machine Comprehension

3: Amazon SageMaker BlazingText

4: Amazon OpenSearch Service

The true class frequency for Romance is the percentage of movies that are actually Romance out of all the movies. This can be calculated by dividing the sum of the true values for Romance by the total number of movies. The predicted class frequency for Adventure is the percentage of movies that are predicted to be Adventure out of all the movies. This can be calculated by dividing the sum of the predicted values for Adventure by the total number of movies. Based on the confusion matrix, the true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%. References: Confusion Matrix, Classification Metrics

 The solution A and C are the most effective in solving the issue while keeping costs to a minimum. The solution A and C involve the following steps:

Configure the endpoint to use Amazon Elastic Inference (EI) accelerators. This will enable the company to reduce the cost and latency of running TensorFlow inference on SageMaker. Amazon EI provides GPU-powered acceleration for deep learning models without requiring the use of GPU instances. Amazon EI can attach to any SageMaker instance type and provide the right amount of acceleration based on the workload1.

Configure the endpoint to automatically scale with the Invocations Per Instance metric. This will enable the company to adjust the number of instances based on the demand and traffic patterns of the website. The Invocations Per Instance metric measures the average number of requests that each instance processes over a period of time. By using this metric, the company can scale out the endpoint when the load increases and scale in when the load decreases. This can improve the response time and availability of the product recommendation engine2.

The other options are not suitable because:

Option B: Creating a new endpoint configuration with two production variants will not solve the issue of increasing response time and errors. Production variants are used to split the traffic between different models or versions of the same model. They can be useful for testing, updating, or A/B testing models. However, they do not provide any scaling or acceleration benefits for the inference workload3.

Option D: Deploying a second instance pool to support a blue/green deployment of models will not solve the issue of increasing response time and errors. Blue/green deployment is a technique for updating models without downtime or disruption. It involves creating a new endpoint configuration with a different instance pool and model version, and then shifting the traffic from the old endpoint to the new endpoint gradually. However, this technique does not provide any scaling or acceleration benefits for the inference workload4.

Option E: Reconfiguring the endpoint to use burstable instances will not solve the issue of increasing response time and errors. Burstable instances are instances that provide a baseline level of CPU performance with the ability to burst above the baseline when needed. They can be useful for workloads that have moderate CPU utilization and occasional spikes. However, they are not suitable for workloads that have high and consistent CPU utilization, such as the product recommendation engine. Moreover, burstable instances may incur additional charges when they exceed their CPU credits5.

1: Amazon Elastic Inference

2: How to Scale Amazon SageMaker Endpoints

3: Deploying Models to Amazon SageMaker Hosting Services

4: Updating Models in Amazon SageMaker Hosting Services

5: Burstable Performance Instances

The data sources that the data scientist should use to augment the dataset of reviews are those that contain relevant and diverse customer feedback about the company or its products. Emails exchanged by customers and the company’s customer service agents can provide valuable insights into the issues and complaints that customers have, as well as the solutions and responses that the company offers. Social media posts containing the name of the company or its products can capture the opinions and sentiments of customers and potential customers, as well as their reactions to marketing campaigns and product launches. A publicly available collection of customer reviews can provide a large and varied sample of feedback from different online platforms and marketplaces, which can help to generalize the ML models and avoid bias.

Detect sentiment from customer reviews using Amazon Comprehend | AWS Machine Learning Blog

How to Apply Machine Learning to Customer Feedback

The solution C will improve the computational efficiency of the models because it uses Amazon SageMaker Debugger and pruning, which are techniques that can reduce the size and complexity of the convolutional neural network (CNN) models. The solution C involves the following steps:

Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Amazon SageMaker Debugger is a service that can capture and analyze the tensors that are emitted during the training process of machine learning models. Amazon SageMaker Debugger can provide insights into the model performance, quality, and convergence. Amazon SageMaker Debugger can also help to identify and diagnose issues such as overfitting, underfitting, vanishing gradients, and exploding gradients1.

Compute the filter ranks based on the training information. Filter ranking is a technique that can measure the importance of each filter in a convolutional layer based on some criterion, such as the average percentage of zero activations or the L1-norm of the filter weights. Filter ranking can help to identify the filters that have little or no contribution to the model output, and thus can be removed without affecting the model accuracy2.

Apply pruning to remove the low-ranking filters. Pruning is a technique that can reduce the size and complexity of a neural network by removing the redundant or irrelevant parts of the network, such as neurons, connections, or filters. Pruning can help to improve the computational efficiency, memory usage, and inference speed of the model, as well as to prevent overfitting and improve generalization3.

Set the new weights based on the pruned set of filters. After pruning, the model will have a smaller and simpler architecture, with fewer filters in each convolutional layer. The new weights of the model can be set based on the pruned set of filters, either by initializing them randomly or by fine-tuning them from the original weights4.

Run a new training job with the pruned model. The pruned model can be trained again with the same or a different dataset, using the same or a different framework or algorithm. The new training job can use the same or a different configuration of Amazon SageMaker, such as the instance type, the hyperparameters, or the data ingestion mode. The new training job can also use Amazon SageMaker Debugger to monitor and analyze the training process and the model quality5.

The other options are not suitable because:

Option A: Using Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs will not be as effective as using Amazon SageMaker Debugger. Amazon CloudWatch is a service that can monitor and observe the operational health and performance of AWS resources and applications. Amazon CloudWatch can provide metrics, alarms, dashboards, and logs for various AWS services, including Amazon SageMaker. However, Amazon CloudWatch does not provide the same level of granularity and detail as Amazon SageMaker Debugger for the tensors that are emitted during the training process of machine learning models. Amazon CloudWatch metrics are mainly focused on the resource utilization and the training progress, not on the model performance, quality, and convergence6.

Option B: Using Amazon SageMaker Ground Truth to build and run data labeling workflows and collecting a larger labeled dataset with the labeling workflows will not improve the computational efficiency of the models. Amazon SageMaker Ground Truth is a service that can create high-quality training datasets for machine learning by using human labelers. A larger labeled dataset can help to improve the model accuracy and generalization, but it will not reduce the memory, battery, and hardware consumption of the model. Moreover, a larger labeled dataset may increase the training time and cost of the model7.

Option D: Using Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model and increasing the model learning rate will not improve the computational efficiency of the models. Amazon SageMaker Model Monitor is a service that can monitor and analyze the quality and performance of machine learning models that are deployed on Amazon SageMaker endpoints. The ModelLatency metric and the OverheadLatency metric can measure the inference latency of the model and the endpoint, respectively. However, these metrics do not provide any information about the training weights, gradients, biases, and activation outputs of the model, which are needed for pruning. Moreover, increasing the model learning rate will not reduce the size and complexity of the model, but it may affect the model convergence and accuracy.

1: Amazon SageMaker Debugger

2: Pruning Convolutional Neural Networks for Resource Efficient Inference

3: Pruning Neural Networks: A Survey

4: Learning both Weights and Connections for Efficient Neural Networks

5: Amazon SageMaker Training Jobs

6: Amazon CloudWatch Metrics for Amazon SageMaker

7: Amazon SageMaker Ground Truth

Amazon SageMaker Model Monitor

 Initializing the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector will provide the maximum performance boost for the LSTM model. Word2vec is a technique that learns distributed representations of words based on their co-occurrence in a large corpus of text. These representations capture semantic and syntactic similarities between words, which can help the LSTM model better understand the meaning and context of the sentences in the text documents. Using word2vec embeddings that are pretrained on a relevant domain (energy sector) can further improve the performance by reducing the vocabulary mismatch and increasing the coverage of the words in the text documents. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Text Classification with TF-IDF, LSTM, BERT: a comparison of performance

AWS Machine Learning Training - Machine Learning - Exam Preparation Path

The most effective way to encode this categorical feature into a numeric feature is to use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings of vectors of real numbers. Similarity encoding is a technique that transforms categorical features into numerical features by computing the similarity between the categories. Similarity encoding can handle high cardinality and redundancy in categorical features, as it can group similar categories together based on their string similarity. For example, if the column contains the values “aspirin”, “asprin”, and “ibuprofen”, similarity encoding will assign a high similarity score to “aspirin” and “asprin”, and a low similarity score to “ibuprofen”. Similarity encoding can also create embeddings of vectors of real numbers, which can be used as input for machine learning models. Amazon SageMaker Data Wrangler is a feature of Amazon SageMaker that enables you to prepare data for machine learning quickly and easily. You can use SageMaker Data Wrangler to apply similarity encoding to a column of categorical data, and generate embeddings of vectors of real numbers that capture the similarity between the categories1. The other options are either less effective or more complex to implement. Spell checking the column and using one-hot encoding would require additional steps and resources, and may not capture all the misspellings or redundancies. One-hot encoding would also create a large number of features, which could increase the dimensionality and sparsity of the data. Ordinal encoding would assign an arbitrary order to the categories, which could introduce bias or noise in the data. References:

1: Amazon SageMaker Data Wrangler – Amazon Web Services

Amazon Kinesis Video Streams is designed specifically for video ingestion, storage, and live playback, with support for indexing and integration with other analytics tools.

“Amazon Kinesis Video Streams enables you to securely stream video from connected devices to AWS for analytics, machine learning (ML), and other processing. Kinesis Video Streams also supports HLS (HTTP Live Streaming) so users can view video streams in real time.”

This meets the needs of:

Real-time camera ingestion

Live viewing for the security team

Indexed and stored data for the data science teamAnd does so in the most cost-effective and managed way.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository for storing, updating, and sharing machine learning features. It supports both online and offline stores for features, allowing real-time access for online inference and batch access for offline model training. It also tracks feature history, making it easier for data scientists to work with and access relevant feature sets.

This solution provides the necessary storage and access capabilities with high operational efficiency by managing feature history and enabling controlled access through IAM roles, making it a comprehensive choice for the company’s requirements.

Data normalization is a data preprocessing technique that scales the features to a common range, such as [0, 1] or [-1, 1]. This helps reduce the impact of features with high magnitude on the cost function and improves the convergence during backpropagation. Data normalization can be done using different methods, such as min-max scaling, z-score standardization, or unit vector normalization. Data normalization is different from dimensionality reduction, which reduces the number of features; model regularization, which adds a penalty term to the cost function to prevent overfitting; and data augmentation, which increases the amount of data by creating synthetic samples. References:

Data processing options for AI/ML | AWS Machine Learning Blog

Data preprocessing - Machine Learning Lens

How to Normalize Data Using scikit-learn in Python

Normalization | Machine Learning | Google for Developers

The most secure action to protect the data from being accessed and transferred to a remote host by malicious code accidentally installed on the training container is to enable network isolation for training jobs. Network isolation is a feature that allows you to run training and inference containers in internet-free mode, which blocks any outbound network calls from the containers, even to other AWS services such as Amazon S3. Additionally, no AWS credentials are made available to the container runtime environment. This way, you can prevent unauthorized access to your data and resources by malicious code or users. You can enable network isolation by setting the EnableNetworkIsolation parameter to True when you call CreateTrainingJob, CreateHyperParameterTuningJob, or CreateModel.

Run Training and Inference Containers in Internet-Free Mode - Amazon SageMaker

The best way to maintain the privacy and integrity of the data stored in Amazon S3 is to use a combination of VPC endpoints and S3 bucket policies. A VPC endpoint allows the SageMaker notebook instance to access the S3 bucket without going through the public internet. A bucket policy allows the S3 bucket owner to specify which VPCs or VPC endpoints can access the bucket. This way, the data is protected from unauthorized access and tampering. The other options are either insecure (A and D) or inefficient (B). References: Using Amazon S3 VPC Endpoints, Using Bucket Policies and User Policies

A logarithmic transformation is a suitable data transformation for a linear regression model when the data has a skewed distribution, such as when the mode is lower than the median and the median is lower than the mean. A logarithmic transformation can reduce the skewness and make the data more symmetric and normally distributed, which are desirable properties for linear regression. A logarithmic transformation can also reduce the effect of outliers and heteroscedasticity (unequal variance) in the data. An exponential transformation would have the opposite effect of increasing the skewness and making the data more asymmetric. A polynomial transformation may not be able to capture the nonlinearity in the data and may introduce multicollinearity among the transformed variables. A sinusoidal transformation is not appropriate for data that does not have a periodic pattern.

Data Transformation - Scaler Topics

Linear Regression - GeeksforGeeks

Linear Regression - Scribbr

The best solution for this scenario is to set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination, and use the built-in transforms Filter, Map, and RenameField to perform the required transformations. This solution has the following advantages:

It minimizes the effort that is needed to set up infrastructure for the ingestion and transformation, as AWS Glue is a fully managed service that provides a serverless Apache Spark environment, a graphical interface to define data sources and targets, and a code generation feature to create and edit scripts1.

It automates the extraction and transformation process, as AWS Glue can schedule the job to run monthly, and handle the connection, authentication, and configuration of the Amazon Redshift cluster and the S3 bucket2.

It minimizes the load on the Amazon Redshift cluster, as AWS Glue can read the data from the cluster in parallel and use a JDBC connection that supports SSL encryption3.

It performs the required transformations, as AWS Glue can use the built-in transforms Filter, Map, and RenameField to remove the rows with NULL values, split the timestamp column into date and time columns, and rename the card name column, respectively4.

The other solutions are not optimal or suitable, because they have the following drawbacks:

A: Setting up an Amazon EMR cluster and creating an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data is not the most efficient or convenient solution, as it requires more effort and resources to provision, configure, and manage the EMR cluster, and to write and maintain the Spark code5.

B: Setting up an Amazon EC2 instance with a SQL client tool to query the data from the Amazon Redshift cluster directly and export the resulting dataset into a CSV file is not a scalable or reliable solution, as it depends on the availability and performance of the EC2 instance, and the manual execution and upload of the SQL queries and the CSV file6.

D: Using Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket and creating an AWS Lambda function to run the query monthly is not a feasible solution, as Amazon Redshift Spectrum does not support writing data to external tables or S3 buckets, only reading data from them7.

1: What Is AWS Glue? - AWS Glue

2: Populating the Data Catalog - AWS Glue

3: Best Practices When Using AWS Glue with Amazon Redshift - AWS Glue

4: Built-In Transforms - AWS Glue

5: What Is Amazon EMR? - Amazon EMR

6: Amazon EC2 - Amazon Web Services (AWS)

7: Using Amazon Redshift Spectrum to Query External Data - Amazon Redshift

 The TensorFlow estimator configuration that will train the model most cost-effectively is to turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter, turn on managed spot training by setting the use_spot_instances parameter to True, and pass the script to the estimator in the call to the TensorFlow fit() method. This configuration will optimize the model for the target hardware platform, reduce the training cost by using Amazon EC2 Spot Instances, and use the custom Python script without any modification.

SageMaker Training Compiler is a feature of Amazon SageMaker that enables you to optimize your TensorFlow, PyTorch, and MXNet models for inference on a variety of target hardware platforms. SageMaker Training Compiler can improve the inference performance and reduce the inference cost of your models by applying various compilation techniques, such as operator fusion, quantization, pruning, and graph optimization. You can enable SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter to the TensorFlow estimator constructor1.

Managed spot training is another feature of Amazon SageMaker that enables you to use Amazon EC2 Spot Instances for training your machine learning models. Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS Cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices. You can use Spot Instances for various fault-tolerant and flexible applications. You can enable managed spot training by setting the use_spot_instances parameter to True and specifying the max_wait and max_run parameters in the TensorFlow estimator constructor2.

The TensorFlow estimator is a class in the SageMaker Python SDK that allows you to train and deploy TensorFlow models on SageMaker. You can use the TensorFlow estimator to run your own Python script on SageMaker, without any modification. You can pass the script to the estimator in the call to the TensorFlow fit() method, along with the location of your input data. The fit() method starts a SageMaker training job and runs your script as the entry point in the training containers3.

The other options are either less cost-effective or more complex to implement. Adjusting the training script to use distributed data parallelism would require modifying the script and specifying appropriate values for the distribution parameter, which could increase the development time and complexity. Setting the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter would not reduce the cost, as it would only specify the maximum duration of the training job, regardless of the instance type.

1: Optimize TensorFlow, PyTorch, and MXNet models for deployment using Amazon SageMaker Training Compiler | AWS Machine Learning Blog

2: Managed Spot Training: Save Up to 90% On Your Amazon SageMaker Training Jobs | AWS Machine Learning Blog

3: sagemaker.tensorflow — sagemaker 2.66.0 documentation

The solution C is the best option to identify and address training issues with the least development effort. The solution C involves the following steps:

Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues. SageMaker Debugger is a feature of Amazon SageMaker that allows data scientists to monitor, analyze, and debug machine learning models during training. SageMaker Debugger provides a set of built-in rules that can automatically detect common issues and anomalies in model training, such as vanishing or exploding gradients, overfitting, underfitting, low GPU utilization, and more1. The data scientist can use the vanishing_gradient rule to check if the gradients are becoming too small and causing the training to not converge. The data scientist can also use the LowGPUUtilization rule to check if the GPU resources are underutilized and causing the training to be inefficient2.

Launch the StopTrainingJob action if issues are detected. SageMaker Debugger can also take actions based on the status of the rules. One of the actions is StopTrainingJob, which can terminate the training job if a rule is in an error state. This can help the data scientist to save time and money by stopping the training early if issues are detected3.

The other options are not suitable because:

Option A: Using CPU utilization metrics that are captured in Amazon CloudWatch and configuring a CloudWatch alarm to stop the training job early if low CPU utilization occurs will not identify and address training issues effectively. CPU utilization is not a good indicator of model training performance, especially for GPU instances. Moreover, CloudWatch alarms can only trigger actions based on simple thresholds, not complex rules or conditions4.

Option B: Using high-resolution custom metrics that are captured in Amazon CloudWatch and configuring an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected will incur more development effort than using SageMaker Debugger. The data scientist will have to write the code for capturing, sending, and analyzing the custom metrics, as well as for invoking the Lambda function and stopping the training job. Moreover, this solution may not be able to detect all the issues that SageMaker Debugger can5.

Option D: Using the SageMaker Debugger confusion and feature_importance_overweight built-in rules and launching the StopTrainingJob action if issues are detected will not identify and address training issues effectively. The confusion rule is used to monitor the confusion matrix of a classification model, which is not relevant for a regression model that predicts prices. The feature_importance_overweight rule is used to check if some features have too much weight in the model, which may not be related to the convergence or resource utilization issues2.

1: Amazon SageMaker Debugger

2: Built-in Rules for Amazon SageMaker Debugger

3: Actions for Amazon SageMaker Debugger

4: Amazon CloudWatch Alarms

5: Amazon CloudWatch Custom Metrics

Using log-scaled hyperparameters is a guideline that can improve the automatic model tuning in Amazon SageMaker. Log-scaled hyperparameters are hyperparameters that have values that span several orders of magnitude, such as learning rate, regularization parameter, or number of hidden units. Log-scaled hyperparameters can be specified by using a log-uniform distribution, which assigns equal probability to each order of magnitude within a range. For example, a log-uniform distribution between 0.001 and 1000 can sample values such as 0.001, 0.01, 0.1, 1, 10, 100, or 1000 with equal probability. Using log-scaled hyperparameters can allow the hyperparameter optimization feature to search the hyperparameter space more efficiently and effectively, as it can explore different scales of values and avoid sampling values that are too small or too large. Using log-scaled hyperparameters can also help avoid numerical issues, such as underflow or overflow, that may occur when using linear-scaled hyperparameters. Using log-scaled hyperparameters can be done by setting the ScalingType parameter to Logarithmic when defining the hyperparameter ranges in Amazon SageMaker12

The other options are not valid or relevant guidelines for improving the automatic model tuning in Amazon SageMaker. Choosing the maximum number of hyperparameters supported by Amazon SageMaker to search the largest number of combinations possible is not a good practice, as it can increase the time and cost of the tuning job and make it harder to find the optimal values. Amazon SageMaker supports up to 20 hyperparameters for tuning, but it is recommended to choose only the most important and influential hyperparameters for the model and algorithm, and use default or fixed values for the rest3 Specifying a very large hyperparameter range to allow Amazon SageMaker to cover every possible value is not a good practice, as it can result in sampling values that are irrelevant or impractical for the model and algorithm, and waste the tuning budget. It is recommended to specify a reasonable and realistic hyperparameter range based on the prior knowledge and experience of the model and algorithm, and use the results of the tuning job to refine the range if needed4 Executing only one hyperparameter tuning job at a time and improving tuning through successive rounds of experiments is not a good practice, as it can limit the exploration and exploitation of the hyperparameter space and make the tuning process slower and less efficient. It is recommended to use parallelism and concurrency to run multiple training jobs simultaneously and leverage the Bayesian optimization algorithm that Amazon SageMaker uses to guide the search for the best hyperparameter values5

To adapt a previously trained model to a new but related task efficiently, the best practice is to leverage transfer learning. This involves retaining the learned features from the earlier model and only retraining the final layers to accommodate the new classification categories.​

In the context of a hybrid architecture combining a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN):​

CNN Component: The CNN is responsible for extracting spatial features from video frames. Since the early layers of a CNN capture generic features like edges and textures, they are often transferable across tasks. Therefore, only the last fully connected layer, which maps these features to specific object classes, needs to be reinitialized and retrained for the new set of objects.​

RNN Component: The RNN handles the temporal dynamics of the sequence data. Similar to the CNN, the earlier layers of the RNN capture general sequence patterns. Thus, reinitializing and retraining only the last layer of the RNN allows the model to adapt to the new prediction task without the need to retrain the entire network.​

This approach minimizes training time and computational resources while effectively adapting the model to new tasks.

 Amazon EMR is a service that can process large amounts of data efficiently and cost-effectively. It can run distributed frameworks such as Apache Spark, which can perform feature engineering on big data. Amazon SageMaker SDK is a Python library that can interact with Amazon SageMaker service to train and deploy machine learning models. It can also use Amazon EMR as a data source for training data. References:

Amazon EMR

Amazon SageMaker SDK

The best visualization for this task is to create a bar plot, faceted by year, of average sales for each region and add a horizontal line in each facet to represent average sales. This way, the data scientist can easily compare the yearly average sales for each region with the overall average sales and see the trends over time. The bar plot also allows the data scientist to see the relative performance of each region within each year and across years. The other options are less effective because they either do not show the yearly trends, do not show the overall average sales, or do not group the data by region.

pandas.DataFrame.groupby — pandas 2.1.4 documentation

pandas.DataFrame.plot.bar — pandas 2.1.4 documentation

Matplotlib - Bar Plot - Online Tutorials Library

Normalization is a data preprocessing technique that can be used to scale the input features to a common range, such as [-1, 1] or [0, 1]. Normalization can help reduce the effect of outliers, improve the convergence of gradient-based algorithms, and prevent variables with a larger magnitude from dominating the model. One common method of normalization is standardization, which transforms each feature to have a mean of 0 and a variance of 1. This can be done by subtracting the mean and dividing by the standard deviation of each feature. Standardization can be useful for models that assume the input features are normally distributed, such as linear regression, logistic regression, and support vector machines. References:

Data normalization and standardization: A video that explains the concept and benefits of data normalization and standardization.

Standardize or Normalize?: A blog post that compares different methods of scaling the input features.

The best way to handle the missing values in the patient age feature is to replace them with the mean or median value from the dataset. This is a common technique for imputing missing values that preserves the overall distribution of the data and avoids introducing bias or reducing the sample size. Dropping the records or the feature would result in losing valuable information and reducing the accuracy of the model. Using k-means clustering would not be appropriate for handling missing values in a single feature, as it is a method for grouping similar data points based on multiple features.

Effective Strategies to Handle Missing Values in Data Analysis

How To Handle Missing Values In Machine Learning Data With Weka

How to handle missing values in Python - Machine Learning Plus

A neural network pre-trained on ImageNet is a deep learning model that has been trained on a large dataset of images containing 1000 classes of objects. The model can learn to extract high-level features from the images that capture the semantic and visual information of the objects. The penultimate layer of the model is the layer before the final output layer, and it contains a feature vector that represents the input image in a lower-dimensional space. By running images through a pre-trained neural network and collecting the feature vectors from the penultimate layer, the Specialist can obtain relevant numerical representations that can be used for nearest-neighbor searches. The feature vectors can capture the similarity between images based on the presence and appearance of similar objects, and they can be compared using distance metrics such as Euclidean distance or cosine similarity. This approach can enable the recommendation engine to show a picture that captures similar objects to a given picture.

ImageNet - Wikipedia

How to use a pre-trained neural network to extract features from images | by Rishabh Anand | Analytics Vidhya | Medium

Image Similarity using Deep Ranking | by Aditya Oke | Towards Data Science

The data scientist should use the area under the precision-recall curve and the true positive rate to optimize the model. These metrics are suitable for imbalanced classification problems, such as credit card fraud detection, where the positive class (fraudulent transactions) is much rarer than the negative class (non-fraudulent transactions).

The area under the precision-recall curve (AUPRC) is a measure of how well the model can identify the positive class among all the predicted positives. Precision is the fraction of predicted positives that are actually positive, and recall is the fraction of actual positives that are correctly predicted. A higher AUPRC means that the model can achieve a higher precision with a higher recall, which is desirable for fraud detection.

The true positive rate (TPR) is another name for recall. It is also known as sensitivity or hit rate. It measures the proportion of actual positives that are correctly identified by the model. A higher TPR means that the model can capture more positives, which is the company’s goal.

Metrics for Imbalanced Classification in Python - Machine Learning Mastery

Precision-Recall - scikit-learn

To comply with the company’s security requirement that SageMaker notebook instances must not have internet connectivity, the most secure and recommended approach is to use VPC interface endpoints (AWS PrivateLink) for connecting SageMaker to other AWS services such as Amazon S3, without requiring internet access or a NAT Gateway.

Exact Extract from Amazon SageMaker documentation:

“You can connect to your notebook instance from your VPC through an interface endpoint in your Virtual Private Cloud (VPC) instead of connecting over the public internet. When you use a VPC interface endpoint, communication between your VPC and the notebook instance is conducted entirely and securely within the AWS network.”

Further security can be enforced using IAM and security group restrictions:

“To restrict access to only connections made from within your VPC, create an AWS Identity and Access Management (IAM) policy that restricts access to only calls that come from within your VPC. Then add that policy to every AWS Identity and Access Management user, group, or role used to access the notebook instance.”

Also:

“Use VPC endpoints to keep traffic between your VPC and SageMaker in the AWS network. This is especially useful when your notebook instances or endpoints don’t have internet access.”

By configuring interface VPC endpoints and controlling security groups and IAM policies appropriately, the SageMaker notebook instances can operate without any internet connectivity, meeting the company’s policy securely and effectively.

 The business problem of predicting whether a new credit card applicant will default on a credit card payment can be framed as a binary classification problem. Binary classification is the task of predicting a discrete class label output for an example, where the class label can only take one of two possible values. In this case, the class label can be either “default” or “no default”, indicating whether the applicant will or will not default on a credit card payment. A binary classification model can return the probability that a given applicant belongs to each class, and then assign the applicant to the class with the highest probability. For example, if the model predicts that an applicant has a 0.8 probability of defaulting and a 0.2 probability of not defaulting, then the model will classify the applicant as “default”. Binary classification is suitable for this problem because the outcome of interest is categorical and binary, and the model needs to return the probability of each outcome.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Classification vs Regression in Machine Learning

The correct combination of actions to enable the data scientist’s IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:

B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted at the endpoint1.

C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries2.

E: Including the SQL statement “USING EXTERNAL FUNCTION ml_function_name” in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML models from SQL statements3.

The other options are not correct or necessary, because they have the following drawbacks:

A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model4.

D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this scenario5.

F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.

1: InvokeEndpoint - Amazon SageMaker

2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena

3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog

4: AmazonAthenaFullAccess - AWS Identity and Access Management

5: GetRecord - Amazon SageMaker Feature Store Runtime

[Invoke a Multi-Model Endpoint - Amazon SageMaker]

Amazon SageMaker Experiments is a service that helps track, compare, and evaluate different iterations of ML models. It can be used to capture key parameters such as the number of features and the sample count from a PySpark script that runs as a SageMaker processing job. A SageMaker processing job is a flexible and scalable way to run data processing workloads on AWS, such as feature engineering, data validation, model evaluation, and model interpretation.

Amazon SageMaker Experiments

Process Data and Evaluate Models

 The solution A will meet the requirements with the least amount of effort from the internal team because it uses Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels, which are fully managed services that can provide the desired functionality. The solution A involves the following steps:

Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Amazon SageMaker Ground Truth is a service that can create high-quality training datasets for machine learning by using human labelers. A private workforce is a group of labelers that the company can manage and control. The internal team can use the private workforce to label the satellite images as having solar panels or not. The SageMaker Ground Truth active learning feature can reduce the labeling effort by using a machine learning model to automatically label the easy examples and only send the difficult ones to the human labelers1.

Use Amazon Rekognition Custom Labels for model training and hosting. Amazon Rekognition Custom Labels is a service that can train and deploy custom machine learning models for image analysis. Amazon Rekognition Custom Labels can use the labeled data from SageMaker Ground Truth to train a model that can detect solar panels in satellite images. Amazon Rekognition Custom Labels can also host the model and provide an API endpoint for inference2.

The other options are not suitable because:

Option B: Setting up a private workforce that consists of the internal team, using the private workforce to label the data, and using Amazon Rekognition Custom Labels for model training and hosting will incur more effort from the internal team than using SageMaker Ground Truth active learning feature. The internal team will have to label all the images manually, without the assistance of the machine learning model that can automate some of the labeling tasks1.

Option C: Setting up a private workforce that consists of the internal team, using the private workforce and the SageMaker Ground Truth active learning feature to label the data, using the SageMaker Object Detection algorithm to train a model, and using SageMaker batch transform for inference will incur more operational overhead than using Amazon Rekognition Custom Labels. The company will have to manage the SageMaker training job, the model artifact, and the batch transform job. Moreover, SageMaker batch transform is not suitable for real-time inference, as it processes the data in batches and stores the results in Amazon S33.

Option D: Setting up a public workforce, using the public workforce to label the data, using the SageMaker Object Detection algorithm to train a model, and using SageMaker batch transform for inference will incur more operational overhead and cost than using a private workforce and Amazon Rekognition Custom Labels. A public workforce is a group of labelers from Amazon Mechanical Turk, a crowdsourcing marketplace. The company will have to pay the public workforce for each labeling task, and it may not have full control over the quality and security of the labeled data. The company will also have to manage the SageMaker training job, the model artifact, and the batch transform job, as explained in option C4.

1: Amazon SageMaker Ground Truth

2: Amazon Rekognition Custom Labels

3: Amazon SageMaker Object Detection

4: Amazon Mechanical Turk

The best services to use for building a data ingestion solution for the company’s Amazon S3-based data lake are:

AWS Glue as the data catalog: AWS Glue is a fully managed extract, transform, and load (ETL) service that can discover, crawl, and catalog data from various sources and formats, and make it available for analysis. AWS Glue can also generate ETL code in Python or Scala to transform, enrich, and join data using AWS Glue Data Catalog as the metadata repository. AWS Glue Data Catalog is a central metadata store that integrates with Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum, allowing users to create a unified view of their data across various sources and formats.

Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights: Amazon Kinesis Data Streams is a service that enables users to collect, process, and analyze real-time streaming data at any scale. Users can create data streams that can capture data from various sources, such as web and mobile applications, IoT devices, and social media platforms. Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data using standard SQL queries or Apache Flink applications. Users can create real-time dashboards, metrics, and alerts based on the streaming data analysis results.

Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics: Amazon Kinesis Data Firehose is a service that enables users to load streaming data into data lakes, data stores, and analytics services. Users can configure Kinesis Data Firehose to automatically deliver data to various destinations, such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and third-party solutions. For clickstream analytics, users can use Kinesis Data Firehose to deliver data to Amazon OpenSearch Service, a fully managed service that offers search and analytics capabilities for log data. Users can use Amazon OpenSearch Service to perform interactive analysis and visualization of clickstream data using Kibana, an open-source tool that is integrated with Amazon OpenSearch Service.

Amazon EMR to generate personalized product recommendations: Amazon EMR is a service that enables users to run distributed data processing frameworks, such as Apache Spark, Apache Hadoop, and Apache Hive, on scalable clusters of EC2 instances. Users can use Amazon EMR to perform advanced analytics, such as machine learning, on large and complex datasets stored in Amazon S3 or other sources. For product recommendations, users can use Amazon EMR to run Spark MLlib, a library that provides scalable machine learning algorithms, such as collaborative filtering, to generate personalized recommendations based on user behavior and preferences.

AWS Glue - Fully Managed ETL Service

Amazon Kinesis - Data Streaming Service

Amazon OpenSearch Service - Managed OpenSearch Service

Amazon EMR - Managed Hadoop Framework

Feature selection is the process of reducing the number of input variables to those that are most relevant for predicting the target variable. One way to do this is to run a correlation check of all features against the target variable and remove features with low target variable correlation scores. This means that these features have little or no linear relationship with the target variable and are not useful for the prediction. This can reduce the model’s complexity and improve its performance. References:

Feature engineering - Machine Learning Lens

Feature Selection For Machine Learning in Python

An Amazon S3 data lake is a cost-effective solution for storing and analyzing large amounts of time-based training data for a new model. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any amount of data in any format. Amazon S3 also offers low-cost storage classes, such as S3 Standard-IA and S3 One Zone-IA, that can reduce the storage costs for infrequently accessed data. By using hourly object prefixes, the Machine Learning Specialist can organize the data into logical partitions based on the time of ingestion. This can enable efficient data access and management, as well as support incremental updates and deletes. The Specialist can also use Amazon S3 lifecycle policies to automatically transition the data to lower-cost storage classes or delete the data after a certain period of time. This way, the Specialist can always train on the last 24 hours of the data and optimize the storage costs.

What is a data lake? - Amazon Web Services

Amazon S3 Storage Classes - Amazon Simple Storage Service

Managing your storage lifecycle - Amazon Simple Storage Service

Best Practices Design Patterns: Optimizing Amazon S3 Performance

To access an Amazon S3 bucket in another account that is encrypted using SSE-KMS, the following are required:

A. An AWS KMS key policy that allows access to the customer master key (CMK). The CMK is the encryption key that is used to encrypt and decrypt the data in the S3 bucket. The KMS key policy defines who can use and manage the CMK. To allow access to the CMK from another account, the key policy must include a statement that grants the necessary permissions (such as kms:Decrypt) to the principal from the other account (such as the SageMaker notebook IAM role).

B. A SageMaker notebook security group that allows access to Amazon S3. A security group is a virtual firewall that controls the inbound and outbound traffic for the SageMaker notebook instance. To allow the notebook instance to access the S3 bucket, the security group must have a rule that allows outbound traffic to the S3 endpoint on port 443 (HTTPS).

C. An IAM role that allows access to the specific S3 bucket. An IAM role is an identity that can be assumed by the SageMaker notebook instance to access AWS resources. The IAM role must have a policy that grants the necessary permissions (such as s3:GetObject) to access the specific S3 bucket. The policy must also include a condition that allows access to the CMK in the other account.

The following are not required or correct:

D. A permissive S3 bucket policy. A bucket policy is a resource-based policy that defines who can access the S3 bucket and what actions they can perform. A permissive bucket policy is not required and not recommended, as it can expose the bucket to unauthorized access. A bucket policy should follow the principle of least privilege and grant the minimum permissions necessary to the specific principals that need access.

E. An S3 bucket owner that matches the notebook owner. The S3 bucket owner and the notebook owner do not need to match, as long as the bucket owner grants cross-account access to the notebook owner through the KMS key policy and the bucket policy (if applicable).

F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3. A subnet ACL is a network access control list that acts as an optional layer of security for the SageMaker notebook instance’s subnet. A subnet ACL is not required to access the S3 bucket, as the security group is sufficient to control the traffic. However, if a subnet ACL is used, it must not block the traffic to the S3 endpoint.

To build a conversational interface that can use natural language to get data from the reports, the company can use a combination of services that can handle both written and spoken inputs, understand the user’s intent and query, and extract the relevant information from the reports. The services that can be used for this purpose are:

Amazon Lex: A service for building conversational interfaces into any application using voice and text. Amazon Lex can create chatbots that can interact with users using natural language, and integrate with other AWS services such as Amazon Connect, Amazon Comprehend, and Amazon Transcribe. Amazon Lex can also use lambda functions to implement the business logic and fulfill the user’s requests.

Amazon Comprehend: A service for natural language processing and text analytics. Amazon Comprehend can analyze text and speech inputs and extract insights such as entities, key phrases, sentiment, syntax, and topics. Amazon Comprehend can also use custom classifiers and entity recognizers to identify specific terms and concepts that are relevant to the domain of the reports.

Amazon Transcribe: A service for speech-to-text conversion. Amazon Transcribe can transcribe audio inputs into text outputs, and add punctuation and formatting. Amazon Transcribe can also use custom vocabularies and language models to improve the accuracy and quality of the transcription for the specific domain of the reports.

Therefore, the company can use the following architecture to build the conversational interface:

Use Amazon Lex to create a chatbot that can accept both written and spoken inputs from the executives. The chatbot can use intents, utterances, and slots to capture the user’s query and parameters, such as the report name, date, metric, or filter.

Use Amazon Transcribe to convert the spoken inputs into text outputs, and pass them to Amazon Lex. Amazon Transcribe can use a custom vocabulary and language model to recognize the terms and concepts related to the reports.

Use Amazon Comprehend to analyze the text inputs and outputs, and extract the relevant information from the reports. Amazon Comprehend can use a custom classifier and entity recognizer to identify the report name, date, metric, or filter from the user’s query, and the corresponding data from the reports.

Use a lambda function to implement the business logic and fulfillment of the user’s query, such as retrieving the data from the reports, performing calculations or aggregations, and formatting the response. The lambda function can also handle errors and validations, and provide feedback to the user.

Use Amazon Lex to return the response to the user, either in text or speech format, depending on the user’s preference.

What Is Amazon Lex?

What Is Amazon Comprehend?

What Is Amazon Transcribe?

Switching to an instance type that has a CPU: GPU ratio of 6:1 will reduce the training costs by using fewer CPUs and GPUs, while maintaining the same level of performance. The GPU idle time indicates that the CPU is not able to feed the GPU with enough data, so reducing the CPU: GPU ratio will balance the workload and improve the GPU utilization. A lower CPU: GPU ratio also means less overhead for inter-process communication and synchronization between the CPU and GPU processes. References:

Optimizing GPU utilization for AI/ML workloads on Amazon EC2

Analyze CPU vs. GPU Performance for AWS Machine Learning

 Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe are AWS machine learning services that can analyze and extract metadata from images, text, audio, and video content. These services are easy to use, scalable, and do not require any machine learning expertise. They can help the media company to quickly index its assets and enable rapid identification of relevant content by the research team. Using these services is the fastest route to index the assets, compared to the other options that involve human intervention, custom model development, or additional steps. References:

AWS Media Intelligence Solutions

AWS Machine Learning Services

The Best Services For Running Machine Learning Models On AWS

The solution that the data scientist should use to improve the performance of the model is to apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset, and retrain the model with the updated training data. This solution can address the problem of class imbalance in the dataset, which can affect the model’s ability to learn from the rare but important positive class (fraud).

Class imbalance is a common issue in machine learning, especially for classification tasks. It occurs when one class (usually the positive or target class) is significantly underrepresented in the dataset compared to the other class (usually the negative or non-target class). For example, in the credit card fraud detection problem, the positive class (fraud) is much less frequent than the negative class (fair transactions). This can cause the model to be biased towards the majority class, and fail to capture the characteristics and patterns of the minority class. As a result, the model may have a high overall accuracy, but a low recall or true positive rate for the minority class, which means it misses many fraudulent transactions.

SMOTE is a technique that can help mitigate the class imbalance problem by generating synthetic samples for the minority class. SMOTE works by finding the k-nearest neighbors of each minority class instance, and randomly creating new instances along the line segments connecting them. This way, SMOTE can increase the number and diversity of the minority class instances, without duplicating or losing any information. By applying SMOTE on the minority class in the training dataset, the data scientist can balance the classes and improve the model’s performance on the positive class1.

The other options are either ineffective or counterproductive. Applying SMOTE on the majority class would not balance the classes, but increase the imbalance and the size of the dataset. Undersampling the minority class would reduce the number of instances available for the model to learn from, and potentially lose some important information. Oversampling the majority class would also increase the imbalance and the size of the dataset, and introduce redundancy and overfitting.

1: SMOTE for Imbalanced Classification with Python - Machine Learning Mastery

Recall and precision are two metrics that can be used to evaluate the performance of a classification model. Recall is the ratio of true positives to the total number of actual positives, which measures how well the model can identify all the relevant cases. Precision is the ratio of true positives to the total number of predicted positives, which measures how accurate the model is when it makes a positive prediction. Based on the confusion matrix in the image, we can calculate the recall and precision as follows:

Recall = TP / (TP + FN) = 12 / (12 + 1) = 0.92

Precision = TP / (TP + FP) = 12 / (12 + 3) = 0.8

Where TP is the number of true positives, FN is the number of false negatives, and FP is the number of false positives. Therefore, the recall and precision of the model are 0.92 and 0.8, respectively.

A recurrent neural network (RNN) is a type of neural network that can process sequential data, such as time series, by maintaining a hidden state that captures the temporal dependencies between the inputs. RNNs are well suited for predicting future events based on past observations, such as forecasting engine failures based on sensor readings. To train an RNN model, the data needs to be labeled with the target variable, which in this case is the type and time of the engine fault. This makes the problem a supervised learning problem, where the goal is to learn a mapping from the input sequence (sensor readings) to the output sequence (engine faults). By using an RNN model, the manufacturer can leverage the temporal information in the data and detect patterns that indicate when an engine might need maintenance for a certain fault.

Recurrent Neural Networks - Amazon SageMaker

Use Amazon SageMaker Built-in Algorithms or Pre-trained Models

Recurrent Neural Network Definition | DeepAI

What are Recurrent Neural Networks? An Ultimate Guide for Newbies!

Lee and Carter go Machine Learning: Recurrent Neural Networks - SSRN

The problem involves assigning multiple related, non-mutually exclusive categories (like "yogurt", "snack", and "dairy product") to customer comments referencing specific products. Amazon Comprehend provides custom classification capabilities, and in this case, the appropriate mode is multi-label, not multi-class.

“Use multi-label mode when each document can belong to more than one class. This is appropriate for situations where the categories are not mutually exclusive.”

This allows a single document to be tagged with several relevant labels simultaneously, which is precisely the use case described.

Word embeddings are a type of dense representation of words, which encode semantic meaning in a vector form. These embeddings are typically pre-trained on a large corpus of text data, such as a large set of books, news articles, or web pages, and capture the context in which words are used. Word embeddings can be used as features for a nearest neighbor model, which can be used to find words used in similar contexts. Downloading pre-trained word embeddings is a good way to get started quickly and leverage the strengths of these representations, which have been optimized on a large amount of data. This is likely to result in more accurate and reliable features than other options like one-hot encoding, edit distance, or using Amazon Mechanical Turk to produce synonyms.

The best option is to use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3. This option meets the following requirements:

It ensures that only nonsensitive data is transferred to the cloud by using table mapping to filter out the tables that contain sensitive data1.

It uses IPsec to secure the data transfer by enabling SSL encryption for the AWS DMS endpoint2.

It uploads the data to Amazon S3 each day for model retraining by using the ongoing replication feature of AWS DMS3.

The other options are not as effective or feasible as the option above. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest data through an AWS Site-to-Site VPN connection directly into Amazon S3 is possible, but it requires more steps and resources than using AWS DMS. Also, it does not specify how to filter out the sensitive data from the tables. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest all data through an AWS Site-to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job is also possible, but it is more complex and error-prone than using AWS DMS. Also, it does not use IPsec as required. Using PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection, and then using AWS Glue to move data from Amazon EC2 to Amazon S3 is not feasible, because PostgreSQL logical replication does not support replicating only a subset of data4. Also, it involves unnecessary data movement and additional costs.

Table mapping - AWS Database Migration Service

Using SSL to encrypt a connection to a DB instance - AWS Database Migration Service

Ongoing replication - AWS Database Migration Service

Logical replication - PostgreSQL

Amazon SageMaker Data Wrangler is a tool that helps data scientists and ML developers to prepare data for ML. One of the features of Data Wrangler is the anomaly detection visualization, which uses an unsupervised ML algorithm to identify outliers in the dataset based on statistical properties. The ML specialist can use this feature to quickly explore the sensor data and find any anomalous values that may affect the model performance. The ML specialist can then add a transformation to a Data Wrangler data flow to remove the outliers from the dataset. The data flow can be exported as a script or a pipeline to automate the data preparation process. This option requires the least operational overhead compared to the other options.

Amazon SageMaker Data Wrangler - Amazon Web Services (AWS)

Anomaly Detection Visualization - Amazon SageMaker

Transform Data - Amazon SageMaker

The company should configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Amazon A2I is a service that allows you to implement human review of machine learning (ML) predictions. It also comes integrated with some of the Artificial Intelligence (AI) services such as Amazon Textract. By using Amazon A2I, the company can perform a manual review on those words that have low confidence scores before performing a business validation. This will help reduce the processing time of loan applications by avoiding errors and rework.

Option A is incorrect because Amazon SageMaker Ground Truth is not a suitable service for human review of Amazon Textract predictions. Amazon SageMaker Ground Truth is a service that helps you build highly accurate training datasets for machine learning. It allows you to label your own data or use a workforce of human labelers. However, it does not provide an easy way to integrate with Amazon Textract and route low-confidence predictions for human review.

Option B is incorrect because using an Amazon Textract synchronous operation instead of an asynchronous operation will not reduce the processing time of loan applications. A synchronous operation is a request-response operation that returns the results immediately. An asynchronous operation is a start-and-check operation that returns a job identifier that you can use to check the status and results later. The choice of operation depends on the size and complexity of the document, not on the confidence of the predictions.

Option D is incorrect because using Amazon Rekognition’s feature to detect text in an image to extract the data from scanned images is not a better alternative than using Amazon Textract. Amazon Rekognition is a service that provides computer vision capabilities, such as face recognition, object detection, and scene analysis. It can also detect text in an image, but it does not provide the same level of accuracy and functionality as Amazon Textract. Amazon Textract can not only detect text, but also extract data from tables and forms, and understand the layout and structure of the document.

Amazon Augmented AI

Amazon SageMaker Ground Truth

Amazon Textract Operations

Amazon Rekognition

 The problem described in the question is a case of overfitting, where the neural network model performs well on the training data but poorly on the test data. This means that the model has learned the noise and specific patterns of the training data, but cannot generalize to new and unseen data. To solve this issue, the Specialist should consider the following changes:

Choose a lower number of layers: Reducing the number of layers can reduce the complexity and capacity of the neural network model, making it less prone to overfitting. A model with 50 hidden layers is likely too deep for the given data size and task. A simpler model with fewer layers can learn the essential features of the data without memorizing the noise.

Enable dropout: Dropout is a regularization technique that randomly drops out some units in the neural network during training. This prevents the units from co-adapting too much and forces the model to learn more robust features. Dropout can improve the generalization and test performance of the model by reducing overfitting.

Enable early stopping: Early stopping is another regularization technique that monitors the validation error during training and stops the training process when the validation error stops decreasing or starts increasing. This prevents the model from overtraining on the training data and reduces overfitting.

Deep Learning - Machine Learning Lens

How to Avoid Overfitting in Deep Learning Neural Networks

How to Identify Overfitting Machine Learning Models in Scikit-Learn

The combination of actions that will meet the requirements with the least operational overhead is to use SageMaker Clarify to automatically detect data bias and to configure SageMaker Data Wrangler to generate a bias report. SageMaker Clarify is a feature of Amazon SageMaker that provides machine learning (ML) developers with tools to gain greater insights into their ML training data and models. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias1. SageMaker Data Wrangler is another feature of Amazon SageMaker that enables you to prepare data for machine learning (ML) quickly and easily. You can use SageMaker Data Wrangler to identify potential bias during data preparation without having to write your own code. You specify input features, such as gender or age, and SageMaker Data Wrangler runs an analysis job to detect potential bias in those features. SageMaker Data Wrangler then provides a visual report with a description of the metrics and measurements of potential bias so that you can identify steps to remediate the bias2. The other actions either require more customization (such as using SageMaker Model Monitor or SageMaker Experiments) or do not meet the requirement of detecting data bias (such as using SageMaker Ground Truth). References:

1: Bias Detection and Model Explainability – Amazon Web Services

2: Amazon SageMaker Data Wrangler – Amazon Web Services

A collaborative filtering recommendation engine is a type of machine learning system that can improve sales for a company by using the large amount of information the company has on users’ behavior and product preferences to predict which products users would like based on the users’ similarity to other users. A collaborative filtering recommendation engine works by finding the users who have similar ratings or preferences for the products, and then recommending the products that the similar users have liked but the target user has not seen or rated. A collaborative filtering recommendation engine can leverage the collective wisdom of the users and discover the hidden patterns and associations among the products and the users. A collaborative filtering recommendation engine can be implemented using Apache Spark ML on Amazon EMR, which are two services that can handle large-scale data processing and machine learning tasks. Apache Spark ML is a library that provides various tools and algorithms for machine learning, such as classification, regression, clustering, recommendation, etc. Apache Spark ML can run on Amazon EMR, which is a service that provides a managed cluster platform that simplifies running big data frameworks, such as Apache Spark, on AWS. Apache Spark ML on Amazon EMR can build a collaborative filtering recommendation engine using the Alternating Least Squares (ALS) algorithm, which is a matrix factorization technique that can learn the latent factors that represent the users and the products, and then use them to predict the ratings or preferences of the users for the products. Apache Spark ML on Amazon EMR can also support both explicit feedback, such as ratings or reviews, and implicit feedback, such as views or clicks, for building a collaborative filtering recommendation engine12

When a model shows poor accuracy on both the training and test datasets, it often indicates underfitting. To improve the model’s accuracy, the data scientist can:

Decrease regularization: Excessive regularization can lead to underfitting by constraining the model too much. Reducing it allows the model to capture more complexity.

Increase the number of training examples: Adding more data can help the model learn better and generalize well, especially if the dataset was previously insufficient.

Increase the number of model features: Adding relevant features can help the model capture more predictive information, thus potentially improving accuracy.

Options A, D, and F would either reduce the complexity or impact the generalization capability, which is not desirable in the case of underfitting.

The solution that will meet the requirements with the least development effort is to use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use, assign the required metadata for each feature, and use Amazon QuickSight to analyze the metadata. This solution can leverage the existing AWS services and features to perform feature-level metadata analysis and reporting.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. The metadata can include information such as data sensitivity, authorship, description, and parameters. The metadata can help make features discoverable, understandable, and traceable. Amazon SageMaker Feature Store allows users to set feature groups for the current features that the ML models use, and assign the required metadata for each feature using the AWS SDK for Python (Boto3), AWS Command Line Interface (AWS CLI), or Amazon SageMaker Studio1.

Amazon QuickSight is a fully managed, serverless business intelligence service that makes it easy to create and publish interactive dashboards that include ML insights. Amazon QuickSight can connect to various data sources, such as Amazon S3, Amazon Athena, Amazon Redshift, and Amazon SageMaker Feature Store, and analyze the data using standard SQL or built-in ML-powered analytics. Amazon QuickSight can also create rich visualizations and reports that can be accessed from any device, and securely shared with anyone inside or outside an organization. Amazon QuickSight can be used to analyze the metadata of the features stored in Amazon SageMaker Feature Store, and generate a report that summarizes the metadata analysis2.

The other options are either more complex or less effective than the proposed solution. Using Amazon SageMaker Data Wrangler to select the features and create a data flow to perform feature-level metadata analysis would require additional steps and resources, and may not capture all the metadata attributes that the company requires. Creating an Amazon DynamoDB table to store feature-level metadata would introduce redundancy and inconsistency, as the metadata is already stored in Amazon SageMaker Feature Store. Using SageMaker Studio to analyze the metadata would not generate a report that can be easily shared and accessed by the company.

1: Amazon SageMaker Feature Store – Amazon Web Services

2: Amazon QuickSight – Business Intelligence Service - Amazon Web Services

The best solution to meet the requirements of the company is to use AWS DataSync to make an initial copy of the entire dataset, and schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS. This is because:

AWS DataSync is an online data movement and discovery service that simplifies data migration and helps you quickly, easily, and securely transfer your file or object data to, from, and between AWS storage services 1. AWS DataSync can copy data between on-premises object storage and Amazon S3, and also supports encryption, scheduling, monitoring, and data integrity validation 1.

AWS DataSync can make an initial copy of the entire dataset by using a DataSync agent, which is a software appliance that connects to your on-premises storage and manages the data transfer to AWS 2. The DataSync agent can be deployed as a virtual machine (VM) on your existing hypervisor, or as an Amazon EC2 instance in your AWS account 2.

AWS DataSync can schedule subsequent incremental transfers of changing data by using a task, which is a configuration that specifies the source and destination locations, the options for the transfer, and the schedule for the transfer 3. You can create a task to run once or on a recurring schedule, and you can also use filters to include or exclude specific files or objects based on their names or prefixes 3.

AWS DataSync can perform the final cutover from on premises to AWS by using a sync task, which is a type of task that synchronizes the data in the source and destination locations 4. A sync task transfers only the data that has changed or that doesn’t exist in the destination, and also deletes any files or objects from the destination that were deleted from the source since the last sync 4.

Therefore, by using AWS DataSync, the company can create a data repository in the AWS Cloud for machine learning projects, and use Amazon S3 for the data storage, while meeting the requirements of encryption, scheduling, monitoring, and data integrity validation.

Data Transfer Service - AWS DataSync

Deploying a DataSync Agent

Creating a Task

Syncing Data with AWS DataSync

Amazon SageMaker’s Neural Topic Model (NTM) is designed to uncover underlying topics within text data by clustering documents based on topic similarity. For document categorization, NTM can identify product categories by analyzing and grouping the documents, making it an efficient choice for unsupervised learning where predefined categories do not exist.

This model minimizes operational overhead by allowing automated topic categorization directly within SageMaker, which is more straightforward than custom Docker image setups or using k-means clustering for tokenized data.

 Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Specialty Sample Questions

The best way to retrain the model to meet the requirements is to set the target_recall hyperparameter to 90% and set the binary_classifier_model_selection_criteria hyperparameter to recall_at_target_precision. This will instruct the linear learner algorithm to optimize the model for a high recall score, while maintaining a reasonable precision score. Recall is the proportion of actual positives that were identified correctly, which is important for the company’s goal of reaching at least 90% of the customers who are likely to buy the new product1. Precision is the proportion of positive identifications that were actually correct, which is also relevant for the company’s budget and efficiency2. By setting the target_recall to 90%, the algorithm will try to achieve a recall score of at least 90%, and by setting the binary_classifier_model_selection_criteria to recall_at_target_precision, the algorithm will select the model that has the highest recall score among those that have a precision score equal to or higher than the target precision3. The target precision is automatically set to the median of the precision scores of all the models trained in parallel4.

The other options are not correct or optimal, because they have the following drawbacks:

B: Setting the target_precision hyperparameter to 90% and setting the binary_classifier_model_selection_criteria hyperparameter to precision_at_target_recall will optimize the model for a high precision score, while maintaining a reasonable recall score. However, this is not aligned with the company’s goal of reaching at least 90% of the customers who are likely to buy the new product, as precision does not reflect how well the model identifies the actual positives1. Moreover, setting the target_precision to 90% might be too high and unrealistic for the dataset, as the current precision score is only 75%4.

C: Using 90% of the historical data for training and setting the number of epochs to 20 will not necessarily improve the recall score of the model, as it does not change the optimization objective or the model selection criteria. Moreover, using more data for training might reduce the amount of data available for validation, which is needed for selecting the best model among the ones trained in parallel3. The number of epochs is also not a decisive factor for the recall score, as it depends on the learning rate, the optimizer, and the convergence of the algorithm5.

D: Setting the normalize_label hyperparameter to true and setting the number of classes to 2 will not affect the recall score of the model, as these are irrelevant hyperparameters for binary classification problems. The normalize_label hyperparameter is only applicable for regression problems, as it controls whether the label is normalized to have zero mean and unit variance3. The number of classes hyperparameter is only applicable for multiclass classification problems, as it specifies the number of output classes3.

1: Classification: Precision and Recall | Machine Learning | Google for Developers

2: Precision and recall - Wikipedia

3: Linear Learner Algorithm - Amazon SageMaker

4: How linear learner works - Amazon SageMaker

5: Getting hands-on with Amazon SageMaker Linear Learner - Pluralsight

Amazon SageMaker notebook instances are fully managed environments that provide an integrated Jupyter notebook interface for data exploration, analysis, and machine learning. Amazon SageMaker notebook instances are based on EC2 instances that run within AWS service accounts, not within customer accounts. This means that the ML Specialist cannot find the Amazon SageMaker notebook instance’s EC2 instance or EBS volume within the VPC, as they are not visible or accessible to the customer. However, the ML Specialist can still take a snapshot of the EBS volume by using the Amazon SageMaker console or API. The ML Specialist can also use VPC interface endpoints to securely connect the Amazon SageMaker notebook instance to the resources within the VPC, such as Amazon S3 buckets, Amazon EFS file systems, or Amazon RDS databases

The Data Scientist needs to migrate an existing on-premises ETL process to the cloud, using a solution that can combine multiple data sources, reuse existing PySpark logic, run on the existing schedule, and minimize the number of servers that need to be managed. The best architecture for this scenario is to use AWS Glue, which is a serverless data integration service that can create and run ETL jobs on AWS.

AWS Glue can perform the following tasks to meet the requirements:

Combine multiple data sources: AWS Glue can access data from various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, Amazon DynamoDB, and more. AWS Glue can also crawl the data sources and discover their schemas, formats, and partitions, and store them in the AWS Glue Data Catalog, which is a centralized metadata repository for all the data assets.

Reuse existing PySpark logic: AWS Glue supports writing ETL scripts in Python or Scala, using Apache Spark as the underlying execution engine. AWS Glue provides a library of built-in transformations and connectors that can simplify the ETL code. The Data Scientist can write the ETL job in PySpark and leverage the existing logic to perform the data processing.

Run the solution on the existing schedule: AWS Glue can create triggers that can start ETL jobs based on a schedule, an event, or a condition. The Data Scientist can create a new AWS Glue trigger to run the ETL job based on the existing schedule, using a cron expression or a relative time interval.

Minimize the number of servers that need to be managed: AWS Glue is a serverless service, which means that it automatically provisions, configures, scales, and manages the compute resources required to run the ETL jobs. The Data Scientist does not need to worry about setting up, maintaining, or monitoring any servers or clusters for the ETL process.

Therefore, the Data Scientist should use the following architecture to build the cloud solution:

Write the raw data to Amazon S3: The Data Scientist can use any method to upload the raw data from the on-premises sources to Amazon S3, such as AWS DataSync, AWS Storage Gateway, AWS Snowball, or AWS Direct Connect. Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data.

Create an AWS Glue ETL job to perform the ETL processing against the input data: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue ETL job. The Data Scientist can specify the input and output data sources, the IAM role, the security configuration, the job parameters, and the PySpark script location. The Data Scientist can also use the AWS Glue Studio, which is a graphical interface that can help design, run, and monitor ETL jobs visually.

Write the ETL job in PySpark to leverage the existing logic: The Data Scientist can use a code editor of their choice to write the ETL script in PySpark, using the existing logic to transform the data. The Data Scientist can also use the AWS Glue script editor, which is an integrated development environment (IDE) that can help write, debug, and test the ETL code. The Data Scientist can store the ETL script in Amazon S3 or GitHub, and reference it in the AWS Glue ETL job configuration.

Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue trigger. The Data Scientist can specify the name, type, and schedule of the trigger, and associate it with the AWS Glue ETL job. The trigger will start the ETL job according to the defined schedule.

Configure the output target of the ETL job to write to a “processed” location in Amazon S3 that is accessible for downstream use: The Data Scientist can specify the output location of the ETL job in the PySpark script, using the AWS Glue DynamicFrame or Spark DataFrame APIs. The Data Scientist can write the output data to a “processed” location in Amazon S3, using a format such as Parquet, ORC, JSON, or CSV, that is suitable for downstream processing.

What Is AWS Glue?

AWS Glue Components

AWS Glue Studio

AWS Glue Triggers

The SageMaker CreatePresignedDomainUrl API is the best option to meet the requirements of the company. This API creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain’s Amazon Elastic File System (EFS) volume. This API can only be called when the authentication mode equals IAM, which means the company can use its existing IdP to authenticate users. The company can use the DynamoDB table to store the domain IDs and user profile names for each department, and use the proxy application to query the table and generate the presigned URL for the appropriate domain according to the user’s credentials. The presigned URL is valid only for a specified duration, which can be set by the SessionExpirationDurationInSeconds parameter. This can help enhance the security and prevent unauthorized access to the domains.

The other options are not suitable for the company’s requirements. The SageMaker CreateHumanTaskUi API is used to define the settings for the human review workflow user interface, which is not related to accessing the SageMaker Studio domains. The SageMaker ListHumanTaskUis API is used to return information about the human task user interfaces in the account, which is also not relevant to the company’s use case. The SageMaker CreatePresignedNotebookInstanceUrl API is used to create a URL to connect to the Jupyter server from a notebook instance, which is different from accessing the SageMaker Studio domain.

The best way to ensure that provisioned resources are being utilized effectively is to redeploy the model on an M5 instance and attach Amazon Elastic Inference to the instance. Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. By using Amazon Elastic Inference, you can choose the instance type that is best suited to the overall CPU and memory needs of your application, and then separately configure the amount of inference acceleration that you need with no code changes. This way, you can avoid wasting GPU resources and pay only for what you use.

Option A is incorrect because a batch transform job is not suitable for real-time predictions. Batch transform is a high-performance and cost-effective feature for generating inferences using your trained models. Batch transform manages all of the compute resources required to get inferences. Batch transform is ideal for scenarios where you’re working with large batches of data, don’t need sub-second latency, or need to process data that is stored in Amazon S3.

Option C is incorrect because redeploying the model on a P3dn instance would not improve the resource utilization. P3dn instances are designed for distributed machine learning and high performance computing applications that need high network throughput and packet rate performance. They are not optimized for inference workloads.

Option D is incorrect because deploying the model onto an Amazon ECS cluster using a P3 instance would not ensure that provisioned resources are being utilized effectively. Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. However, using Amazon ECS would not address the issue of underutilized GPU resources. In fact, it might introduce additional overhead and complexity in managing the cluster.

Amazon Elastic Inference - Amazon SageMaker

Batch Transform - Amazon SageMaker

Amazon EC2 P3 Instances

Amazon EC2 P3dn Instances

Amazon Elastic Container Service

 Based on the figure provided, a decision tree would have the highest recall with respect to the fraudulent class. Recall is a model evaluation metric that measures the proportion of actual positive instances that are correctly classified by the model. Recall is calculated as follows:

Recall = True Positives / (True Positives + False Negatives)

A decision tree is a type of machine learning model that can perform classification tasks by splitting the data into smaller and purer subsets based on a series of rules or conditions. A decision tree can handle both linear and non-linear data, and can capture complex patterns and interactions among the features. A decision tree can also be easily visualized and interpreted1

In this case, the data is not linearly separable, and has a clear pattern of seasonality. The fraudulent class forms a large circle in the center of the plot, while the normal class is scattered around the edges. A decision tree can use the transaction month and the age of account as the splitting criteria, and create a circular boundary that separates the fraudulent class from the normal class. A decision tree can achieve a high recall for the fraudulent class, as it can correctly identify most of the black dots as positive instances, and minimize the number of false negatives. A decision tree can also adjust the depth and complexity of the tree to balance the trade-off between recall and precision23

The other options are not valid or suitable for achieving a high recall for the fraudulent class. A linear support vector machine (SVM) is a type of machine learning model that can perform classification tasks by finding a linear hyperplane that maximizes the margin between the classes. A linear SVM can handle linearly separable data, but not non-linear data. A linear SVM cannot capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall4 A naive Bayesian classifier is a type of machine learning model that can perform classification tasks by applying the Bayes’ theorem and assuming conditional independence among the features. A naive Bayesian classifier can handle both linear and non-linear data, and can incorporate prior knowledge and probabilities into the model. However, a naive Bayesian classifier may not perform well when the features are correlated or dependent, as in this case. A naive Bayesian classifier may not capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall5 A single perceptron with sigmoidal activation function is a type of machine learning model that can perform classification tasks by applying a weighted linear combination of the features and a non-linear activation function. A single perceptron with sigmoidal activation function can handle linearly separable data, but not non-linear data. A single perceptron with sigmoidal activation function cannot capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall.

Amazon Kinesis Data Firehose is a fully managed service that can capture, transform, and load streaming data into AWS data stores, such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. It can also invoke AWS Lambda functions to perform custom transformations on the data. Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. It can also use machine learning algorithms, such as Random Cut Forest (RCF), to perform anomaly detection on streaming data. RCF is an unsupervised learning algorithm that assigns an anomaly score to each data point based on how different it is from the rest of the data. By using Kinesis Data Firehose and Kinesis Data Analytics, the cybersecurity company can ingest the data in real time, score the malicious events as anomalies, and stream the results to Amazon S3, which can serve as a data lake for later processing and analysis. This is the most efficient way to accomplish these tasks, as it does not require any additional infrastructure, coding, or training.

Amazon Kinesis Data Firehose - Amazon Web Services

Amazon Kinesis Data Analytics - Amazon Web Services

Anomaly Detection with Amazon Kinesis Data Analytics - Amazon Web Services

[AWS Certified Machine Learning - Specialty Sample Questions]

Amazon Fraud Detector is a managed service designed to detect potentially fraudulent online activities, such as transactions. It uses machine learning and business rules to classify activities as fraudulent or legitimate, minimizing the need for custom model training. By using the Amazon Fraud Detector prediction API, the company can automatically approve or reject transactions flagged as fraudulent, implementing an efficient risk mitigation strategy without extensive operational effort.

This approach requires minimal setup and effectively allows the company to block fraudulent transactions with high confidence, addressing the business's need to balance risk mitigation and customer impact.

 Amazon Forecast requires the input data to be in a specific format. The data scientist should use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. The target time series dataset should contain the timestamp, item_id, and demand columns, while the item metadata dataset should contain the item_id, category, and lead_time columns. Both datasets should be uploaded as .csv files to Amazon S3 . References:

How Amazon Forecast Works - Amazon Forecast

Choosing Datasets - Amazon Forecast

To secure calls to the Amazon SageMaker Service API, the ML specialist should take the following steps:

Modify the security group on the endpoint network interface to restrict access to the instances. This will allow the ML specialist to control which instances in the VPC can communicate with the VPC interface endpoint for the Amazon SageMaker Service API. The security group can specify inbound and outbound rules based on the instance IDs, IP addresses, or CIDR blocks1.

Add a SageMaker Runtime VPC endpoint interface to the VPC. This will allow the ML specialist to invoke the SageMaker endpoints from within the VPC without using the public internet. The SageMaker Runtime VPC endpoint interface connects the VPC directly to the SageMaker Runtime using AWS PrivateLink2.

The other options are not as effective or necessary as the steps above. Adding a VPC endpoint policy to allow access to the IAM users is not required, as the IAM users can already access the Amazon SageMaker Service API through the VPC interface endpoint. Modifying the users’ IAM policy to allow access to Amazon SageMaker Service API calls only is not sufficient, as it does not prevent unauthorized instances from accessing the VPC interface endpoint. Modifying the ACL on the endpoint network interface to restrict access to the instances is not possible, as network ACLs are associated with subnets, not network interfaces3.

Security groups for your VPC - Amazon Virtual Private Cloud

Connect to SageMaker Within your VPC - Amazon SageMaker

Network ACLs - Amazon Virtual Private Cloud

 Quantile binning is a data transformation technique that can be used to handle skewed and non-linear numerical features. It divides the range of a feature into equal-sized bins based on the percentiles of the data. Each bin is assigned a numerical value that represents the midpoint of the bin. This way, the feature values are transformed into a more uniform distribution that can improve the performance of linear models. Quantile binning can also reduce the impact of outliers and noise in the data.

One-hot encoding, Cartesian product transformation, and normalization are not suitable for this scenario. One-hot encoding is used to transform categorical features into binary features. Cartesian product transformation is used to create new features by combining existing features. Normalization is used to scale numerical features to a standard range, but it does not change the shape of the distribution. References:

Data Transformations for Machine Learning

Quantile Binning Transformation

The solution C meets the requirements because it minimizes costs for compute infrastructure, maximizes the scalability of resources for training, and facilitates the use of an ML model in low-connectivity environments. The solution C involves the following steps:

Move the training data to an Amazon S3 bucket. This will enable the company to store the large amount of data in a durable, scalable, and cost-effective way. It will also allow the company to access the data from the cloud for training and evaluation purposes1.

Train and evaluate the model by using Amazon SageMaker. This will enable the company to use a fully managed service that provides various features and tools for building, training, tuning, and deploying ML models. Amazon SageMaker can handle large-scale data processing and distributed training, and it can leverage the power of AWS compute resources such as Amazon EC2, Amazon EKS, and AWS Fargate2.

Optimize the model by using SageMaker Neo. This will enable the company to reduce the size of the model and improve its performance and efficiency. SageMaker Neo can compile the model into an executable that can run on various hardware platforms, such as CPUs, GPUs, and edge devices3.

Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. This will enable the company to deploy the model on a local device that can run inference in real time, even in low-connectivity environments. AWS IoT Greengrass can extend AWS cloud capabilities to the edge, and it can securely communicate with the cloud for updates and synchronization4.

Deploy the model on the edge device. This will enable the company to automate quality control in its facilities by using the model to detect defects in new parts as they move on a conveyor belt. The model can run inference locally on the edge device without requiring internet connectivity, and it can send the results to the cloud when the connection is available4.

The other options are not suitable because:

Option A: Deploying the model on a SageMaker hosting services endpoint will not facilitate the use of the model in low-connectivity environments, as it will require internet access to perform inference. Moreover, it may incur higher costs for hosting and data transfer than deploying the model on an edge device.

Option B: Training and evaluating the model on premises will not minimize costs for compute infrastructure, as it will require the company to maintain and upgrade its own hardware and software. Moreover, it will not maximize the scalability of resources for training, as it will limit the company’s ability to leverage the cloud’s elasticity and flexibility.

Option D: Training the model on premises will not minimize costs for compute infrastructure, nor maximize the scalability of resources for training, for the same reasons as option B.

1: Amazon S3

2: Amazon SageMaker

3: SageMaker Neo

4: AWS IoT Greengrass

 The best feature engineering technique to speed up the model training time without losing a lot of information from the original dataset is to use an autoencoder or principal component analysis (PCA) to replace original features with new features. An autoencoder is a type of neural network that learns a compressed representation of the input data, called the latent space, by minimizing the reconstruction error between the input and the output. PCA is a statistical technique that reduces the dimensionality of the data by finding a set of orthogonal axes, called the principal components, that capture the maximum variance of the data. Both techniques can help reduce the number of features and remove the noise and redundancy in the data, which can improve the model performance and speed up the training process. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Dimensionality Reduction for Machine Learning

AWS Machine Learning Training - Deep Learning with Amazon SageMaker

A residual plot is a type of plot that displays the values of a predictor variable in a regression model along the x-axis and the values of the residuals along the y-axis. This plot is used to assess whether or not the residuals in a regression model are normally distributed and whether or not they exhibit heteroscedasticity. Heteroscedasticity means that the variance of the residuals is not constant across different values of the predictor variable. This violates one of the assumptions of linear regression and can lead to biased estimates and unreliable predictions. The displayed residual plot shows a clear pattern of heteroscedasticity, as the residuals spread out as the fitted values increase. This indicates that linear regression is inappropriate for this data and a different model should be used. References:

Regression - Amazon Machine Learning

How to Create a Residual Plot by Hand

How to Create a Residual Plot in Python

 The ML specialist should run the Model Monitor baseline job again on the new training set and configure Model Monitor to use the new baseline. This is because the baseline job computes the statistics and constraints for the data quality and model quality metrics, which are used to detect violations. If the training set changes, the baseline job should be updated accordingly to reflect the new distribution of the data and the model performance. Otherwise, the old baseline may not be representative of the current production traffic and may cause false alarms or miss violations. References:

Monitor data and model quality - Amazon SageMaker

Detecting and analyzing incorrect model predictions with Amazon SageMaker Model Monitor and Debugger | AWS Machine Learning Blog

 To configure the notebook instance placement to meet the requirements, the Data Science team should associate the Amazon SageMaker notebook with a private subnet in a VPC. A VPC is a virtual network that is logically isolated from other networks in AWS. A private subnet is a subnet that has no internet gateway attached to it, and therefore cannot communicate with the internet. By placing the notebook instance in a private subnet, the team can ensure that it stays within a secured VPC with no internet access.

However, to access data stored in Amazon S3 buckets and other AWS services, the team needs to ensure that the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it. A VPC endpoint is a gateway that enables private connections between the VPC and supported AWS services. A VPC endpoint does not require an internet gateway, a NAT device, or a VPN connection, and ensures that the traffic between the VPC and the AWS service does not leave the AWS network. By using VPC endpoints, the team can access Amazon S3 and Amazon SageMaker from the notebook instance without compromising data privacy or security.

What Is Amazon VPC? - Amazon Virtual Private Cloud

Subnet Routing - Amazon Virtual Private Cloud

VPC Endpoints - Amazon Virtual Private Cloud

Updating the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant is the simplest approach with the least risk to deploy the model and roll it back, if needed. This is because SageMaker supports A/B testing, which allows the Specialist to compare the performance of different model variants by sending a portion of the traffic to each variant. The Specialist can monitor the metrics of each variant and adjust the weights accordingly. If the new variant does not perform as expected, the Specialist can revert traffic to the last version by resetting the weights to 100% for the old variant and 0% for the new variant. This way, the Specialist can deploy the model without affecting the customer experience and roll it back easily if needed. References:

Amazon SageMaker

Deploying models to Amazon SageMaker hosting services

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. SageMaker provides a variety of tools and frameworks to support the entire machine learning workflow, from data preparation to model deployment.

One of the tools that SageMaker offers is the Amazon SageMaker Python SDK, which is a high-level library that simplifies the interaction with SageMaker APIs and services. The SageMaker Python SDK allows you to write code in Python and use popular frameworks such as TensorFlow, PyTorch, MXNet, and more. You can use the SageMaker Python SDK to create and manage SageMaker resources such as notebook instances, training jobs, endpoints, and feature store.

If you need to continue working on a TensorFlow project using SageMaker for training without Wi-Fi access, the best approach is to download the TensorFlow Docker container used in SageMaker from GitHub to your local environment, and use the SageMaker Python SDK to test the code. This way, you can ensure that your code is compatible with the SageMaker environment and avoid any potential issues when you upload your code to SageMaker and start the training job. You can also use the same code to deploy your model to a SageMaker endpoint when you have Wi-Fi access again.

To download the TensorFlow Docker container used in SageMaker, you can visit the SageMaker Docker GitHub repository and follow the instructions to build the image locally. You can also use the SageMaker Studio Image Build CLI to automate the process of building and pushing the Docker image to Amazon Elastic Container Registry (Amazon ECR). To use the SageMaker Python SDK to test the code, you can install the SDK on your local machine by following the installation guide. You can also refer to the TensorFlow documentation for more details on how to use the SageMaker Python SDK with TensorFlow.

SageMaker Docker GitHub repository

SageMaker Studio Image Build CLI

SageMaker Python SDK installation guide

SageMaker Python SDK TensorFlow documentation

The solution C is the most likely to improve the data ingestion rate into Amazon S3 because it increases the number of shards for the data stream. The number of shards determines the throughput capacity of the data stream, which affects the rate of data ingestion. Each shard can support up to 1 MB per second of data input and 2 MB per second of data output. By increasing the number of shards, the company can increase the data ingestion rate proportionally. The company can use the UpdateShardCount API operation to modify the number of shards in the data stream1.

The other options are not likely to improve the data ingestion rate into Amazon S3 because:

Option A: Increasing the number of S3 prefixes for the delivery stream to write to will not affect the data ingestion rate, as it only changes the way the data is organized in the S3 bucket. The number of S3 prefixes can help to optimize the performance of downstream applications that read the data from S3, but it does not impact the performance of Kinesis Data Firehose2.

Option B: Decreasing the retention period for the data stream will not affect the data ingestion rate, as it only changes the amount of time the data is stored in the data stream. The retention period can help to manage the data availability and durability, but it does not impact the throughput capacity of the data stream3.

Option D: Adding more consumers using the Kinesis Client Library (KCL) will not affect the data ingestion rate, as it only changes the way the data is processed by downstream applications. The consumers can help to scale the data processing and handle failures, but they do not impact the data ingestion into S3 by Kinesis Data Firehose4.

1: Resharding - Amazon Kinesis Data Streams

2: Amazon S3 Prefixes - Amazon Kinesis Data Firehose

3: Data Retention - Amazon Kinesis Data Streams

4: Developing Consumers Using the Kinesis Client Library - Amazon Kinesis Data Streams

Amazon Personalize is a fully managed machine learning service that makes it easy for developers to create personalized user experiences at scale. It uses the same recommender system technology that Amazon uses to create its own personalized recommendations. Amazon Personalize provides several pre-built recipes that can be used to train models for different use cases. The USER_PERSONALIZATION recipe is designed to provide personalized recommendations for existing users based on their past interactions with items. The PERSONALIZED_RANKING recipe is designed to re-rank a list of items for a user based on their preferences. The USER_PERSONALIZATION recipe is more suitable for this use case because it can generate recommendations for each user without requiring a list of candidate items. To filter out the items that the user previously purchased, a real-time filter can be created and applied to the campaign. A real-time filter is a dynamic filter that uses the latest interaction data to exclude items from the recommendations. By using Amazon Personalize, the development effort is minimized because it handles the data processing, model training, and deployment automatically. The web application can use the GetRecommendations API operation to get the real-time recommendations from the campaign. References:

Amazon Personalize

What is Amazon Personalize?

USER_PERSONALIZATION recipe

PERSONALIZED_RANKING recipe

Filtering recommendations

GetRecommendations API operation

The Data Scientist should increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights and change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC). This will help reduce the number of false negative predictions by the model.

The scale_pos_weight parameter controls the balance of positive and negative weights in the XGBoost algorithm. It is useful for imbalanced classification problems, such as fraud detection, where the number of positive examples (fraudulent transactions) is much smaller than the number of negative examples (non-fraudulent transactions). By increasing the scale_pos_weight parameter, the Data Scientist can assign more weight to the positive class and make the model more sensitive to detecting fraudulent transactions.

The eval_metric parameter specifies the metric that is used to measure the performance of the model during training and validation. The default metric for binary classification problems is the error rate, which is the fraction of incorrect predictions. However, the error rate is not a good metric for imbalanced classification problems, because it does not take into account the cost of different types of errors. For example, in fraud detection, a false negative (failing to detect a fraudulent transaction) is more costly than a false positive (flagging a non-fraudulent transaction as fraudulent). Therefore, the Data Scientist should use a metric that reflects the trade-off between the true positive rate (TPR) and the false positive rate (FPR), such as the Area Under the ROC Curve (AUC). The AUC is a measure of how well the model can distinguish between the positive and negative classes, regardless of the classification threshold. A higher AUC means that the model can achieve a higher TPR with a lower FPR, which is desirable for fraud detection.

XGBoost Parameters - Amazon Machine Learning

Using XGBoost with Amazon SageMaker - AWS Machine Learning Blog

To capture word context and sequential QA information, the embedding vectors need to consider both the order and the meaning of the words in the text.

Option B, Amazon SageMaker BlazingText algorithm in Skip-gram mode, is a valid option because it can learn word embeddings that capture the semantic similarity and syntactic relations between words based on their co-occurrence in a window of words. Skip-gram mode can also handle rare words better than continuous bag-of-words (CBOW) mode1.

Option E, combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN), is another valid option because it can leverage the advantages of Skip-gram mode and also use an RNN to model the sequential nature of the text. An RNN can capture the temporal dependencies and long-term dependencies between words, which are important for QA tasks2.

Option A, Amazon SageMaker seq2seq algorithm, is not a valid option because it is designed for sequence-to-sequence tasks such as machine translation, summarization, or chatbots. It does not produce embedding vectors for text series, but rather generates an output sequence given an input sequence3.

Option C, Amazon SageMaker Object2Vec algorithm, is not a valid option because it is designed for learning embeddings for pairs of objects, such as text-image, text-text, or image-image. It does not produce embedding vectors for text series, but rather learns a similarity function between pairs of objects4.

Option D, Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode, is not a valid option because it does not capture word context as well as Skip-gram mode. CBOW mode predicts a word given its surrounding words, while Skip-gram mode predicts the surrounding words given a word. CBOW mode is faster and more suitable for frequent words, but Skip-gram mode can learn more meaningful embeddings for rare words1.

1: Amazon SageMaker BlazingText

2: Recurrent Neural Networks (RNNs)

3: Amazon SageMaker Seq2Seq

4: Amazon SageMaker Object2Vec

The problem that the Machine Learning Specialist is facing is likely due to concept drift, which is a phenomenon where the statistical properties of the target variable change over time, making the model less accurate and relevant. Concept drift can occur due to various reasons, such as changes in customer preferences, market trends, product inventory, seasonality, etc. In this case, the product recommendations model may have become outdated as the product inventory changed over time, making the recommendations less appealing to the customers. To address this issue, the model should be periodically retrained using the original training data plus new data as product inventory changes. This way, the model can learn from the latest data and adapt to the changing customer behavior and preferences. Retraining the model from scratch using the original data while adding a regularization term may not be sufficient, as it does not account for the new data. Updating the model’s hyperparameters may not help either, as it does not address the underlying data distribution change. Re-engineering the model completely may not be necessary, as the model may still be valid and useful with periodic retraining.

Concept Drift - Amazon SageMaker

Detecting and Handling Concept Drift - Amazon SageMaker

Machine Learning Concepts - Amazon Machine Learning

Principal Component Analysis (PCA) is a dimensionality reduction technique that captures the variance within the feature space, helping to understand the directions in which data varies most. In SageMaker Data Wrangler, the multicollinearity measurement and PCA features allow the data scientist to analyze interdependencies between predictor variables while reducing redundancy. PCA transforms correlated features into a set of uncorrelated components, helping to simplify the dataset without significant loss of information, making it ideal for refining features based on variance.

Options A and D offer methods to understand feature relevance but are less effective for managing multicollinearity and variance representation in the data.

The data scientist should use K-means and principal component analysis (PCA) to meet this requirement. K-means is a clustering algorithm that can group customers based on their similarity in the feature space. PCA is a dimensionality reduction technique that can transform the original 980 variables into a smaller set of uncorrelated variables that capture most of the variance in the data. This can help reduce the computational cost and noise in the data, and improve the performance of the clustering algorithm.

Clustering - Amazon SageMaker

Dimensionality Reduction - Amazon SageMaker

 The best feature engineering strategy for this scenario is to apply dimensionality reduction by using the principal component analysis (PCA) algorithm. PCA is a technique that transforms a large set of correlated features into a smaller set of uncorrelated features called principal components. This can help reduce the complexity and noise in the data, improve the performance and interpretability of the model, and avoid overfitting. Amazon SageMaker provides a built-in PCA algorithm that can be used to perform dimensionality reduction on tabular data. The ML specialist can use Amazon SageMaker to train and deploy the PCA model, and then use the output of the PCA model as the input for the classification model.

Dimensionality Reduction with Amazon SageMaker

Amazon SageMaker PCA Algorithm

The machine learning model in this scenario shows signs of overfitting, as evidenced by better performance on the training dataset than on the testing dataset. Overfitting indicates that the model is capturing noise or details specific to the training data rather than general patterns.

One common approach to reduce overfitting is L2 regularization, which adds a penalty to the loss function for large weights and helps the model generalize better by smoothing out the weight distribution. By increasing the value of the L2 hyperparameter, the ML specialist can increase this penalty, helping to mitigate overfitting and improve performance on the testing dataset.

Options like increasing momentum or reducing dropout are less effective for addressing overfitting in this context.

The services that are integrated with Amazon SageMaker to track the information that the Machine Learning Specialist needs are AWS CloudTrail and Amazon CloudWatch. AWS CloudTrail is a service that records the API calls and events for AWS services, including Amazon SageMaker. AWS CloudTrail can track the actions performed by the Data Scientists, such as creating notebooks, training models, and deploying endpoints. AWS CloudTrail can also provide information such as the identity of the user, the time of the action, the parameters used, and the response elements returned. AWS CloudTrail can help the Machine Learning Specialist to monitor the usage and activity of Amazon SageMaker, as well as to audit and troubleshoot any issues1 Amazon CloudWatch is a service that collects and analyzes the metrics and logs for AWS services, including Amazon SageMaker. Amazon CloudWatch can track the performance and utilization of the Amazon SageMaker endpoints, such as the CPU and GPU utilization, the inference latency, the number of invocations, etc. Amazon CloudWatch can also track the errors and alarms that are generated when an endpoint is invoked, such as the model errors, the throttling errors, the HTTP errors, etc. Amazon CloudWatch can help the Machine Learning Specialist to optimize the operational performance and reliability of Amazon SageMaker, as well as to set up notifications and actions based on the metrics and logs

Amazon SageMaker Autopilot is a fully managed solution that automatically explores different ML models and selects the most effective ones for a given prediction task. After model training, Amazon SageMaker Clarify can generate feature importance scores, identifying the top features in a straightforward, automated manner with minimal manual intervention.

By using SageMaker Autopilot, the data scientist can obtain the desired feature importance ranking for predictive attributes with minimal setup and low operational overhead, as opposed to manually configuring models in SageMaker.

 The best way to split the dataset into a training and test set for this use case is to randomly select 10% of the users and split off all interaction data from these users for the test set. This is because the company relies on a steady stream of new customers, so the test set should reflect the behavior of new customers who have not been seen by the model before. The other options are not suitable because they either mix old and new customers in the test set (A and B), or they bias the test set towards users with less interaction data ©. References:

Amazon SageMaker Developer Guide: Train and Test Datasets

Amazon Personalize Developer Guide: Preparing and Importing Data

Amazon Kinesis Data Analytics is a service that enables you to analyze streaming data in real time using SQL or Apache Flink applications. You can use Kinesis Data Analytics to process and gain insights from data streams such as web logs, clickstreams, IoT data, and more.

To use SQL to query a data stream of GZIP files, you need to first transform the data into a format that Kinesis Data Analytics can understand, such as JSON, CSV, or Apache Parquet. You can use an AWS Lambda function to perform this transformation and send the output to a Kinesis data stream that is connected to your Kinesis Data Analytics application. This way, you can use SQL to query the stream with the least latency, as Lambda functions are triggered in near real time by the incoming data and Kinesis Data Analytics can process the data as soon as it arrives.

The other options are not optimal for this scenario, as they introduce more latency or complexity. AWS Glue is a serverless data integration service that can perform ETL (extract, transform, and load) tasks on data sources, but it is not designed for real-time streaming data analysis. An Amazon Kinesis Client Library is a Java library that enables you to build custom applications that process data from Kinesis data streams, but it requires more coding and configuration than using a Lambda function. Amazon Kinesis Data Firehose is a service that can deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, but it does not support SQL queries on the data.

What Is Amazon Kinesis Data Analytics for SQL Applications?

Using AWS Lambda with Amazon Kinesis Data Streams

Using AWS Lambda with Amazon Kinesis Data Firehose

 The best solution for implementing the testing model with the least amount of operational overhead is to use the following steps:

Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. This operation allows the developers to update the variant weights and capacities of an existing SageMaker endpoint without deleting and recreating the endpoint. Setting the DesiredWeight parameter to 0 means that the new version of the model will not receive any traffic initially1

Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. This parameter allows the developers to override the variant weights and direct a request to a specific variant. This way, the developers can test the new version of the model for a limited number of users who opted in for the preview feature2

When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version. This operation allows the developers to perform a gradual rollout of the new version of the model and monitor its performance and accuracy. The developers can adjust the variant weights and capacities as needed until the new version of the model serves all the traffic1

The other options are incorrect because they either require more operational overhead or do not support the desired use cases. For example:

Option A uses the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. This operation creates a new endpoint configuration, which requires deleting and recreating the endpoint to apply the changes. This adds extra overhead and downtime for the endpoint. It also does not support the gradual rollout of the new version of the model3

Option B uses two SageMaker hosted endpoints that serve the different versions of the model and an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. This option requires creating and managing additional resources and services, such as the second endpoint and the ALB. It also requires changing the app code to send the query string parameter for the preview feature4

Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job. It also requires the ML specialist to manage the IAM user and the keys.

1: UpdateEndpointWeightsAndCapacities - Amazon SageMaker

2: InvokeEndpoint - Amazon SageMaker

3: CreateEndpointConfig - Amazon SageMaker

4: Application Load Balancer - Elastic Load Balancing

A VPC endpoint is a logical device that enables private connections between a VPC and supported AWS services. A VPC endpoint can be either a gateway endpoint or an interface endpoint. A gateway endpoint is a gateway that is a target for a specified route in the route table, used for traffic destined to a supported AWS service. An interface endpoint is an elastic network interface with a private IP address that serves as an entry point for traffic destined to a supported service1

In this case, the Machine Learning Specialist can create a gateway endpoint for Amazon S3, which is a supported service for gateway endpoints. A gateway endpoint for Amazon S3 enables the VPC to access Amazon S3 privately, without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection. The traffic between the VPC and Amazon S3 does not leave the Amazon network2

To restrict access to the dataset stored in Amazon S3, the Machine Learning Specialist can apply a bucket access policy that allows access only from the given VPC endpoint and the VPC. A bucket access policy is a resource-based policy that defines who can access a bucket and what actions they can perform. A bucket access policy can use various conditions to control access, such as the source IP address, the source VPC, the source VPC endpoint, etc. In this case, the Machine Learning Specialist can use the aws:sourceVpce condition to specify the ID of the VPC endpoint, and the aws:sourceVpc condition to specify the ID of the VPC. This way, only the requests that originate from the VPC endpoint or the VPC can access the bucket that contains the dataset34

The other options are not valid or secure ways to satisfy the requirements. Creating a VPC endpoint and applying a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance is not a good option, as it does not restrict access to the VPC. An Amazon EC2 instance is a virtual server that runs in the AWS cloud. An Amazon EC2 instance can have a public IP address or a private IP address, depending on the network configuration. Allowing access from an Amazon EC2 instance does not guarantee that the instance is in the same VPC as the VPC endpoint, and may expose the dataset to unauthorized access. Creating a VPC endpoint and using Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance is not a good option, as it does not restrict access to the VPC. NACLs are stateless firewalls that can control inbound and outbound traffic at the subnet level. NACLs can use rules to allow or deny traffic based on the protocol, port, and source or destination IP address. However, NACLs do not support VPC endpoints as a source or destination, and cannot filter traffic based on the VPC endpoint ID or the VPC ID. Therefore, using NACLs does not guarantee that the traffic is from the VPC endpoint or the VPC, and may expose the dataset to unauthorized access. Creating a VPC endpoint and using security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance is not a good option, as it does not restrict access to the VPC. Security groups are stateful firewalls that can control inbound and outbound traffic at the instance level. Security groups can use rules to allow or deny traffic based on the protocol, port, and source or destination. However, security groups do not support VPC endpoints as a source or destination, and cannot filter traffic based on the VPC endpoint ID or the VPC ID. Therefore, using security groups does not guarantee that the traffic is from the VPC endpoint or the VPC, and may expose the dataset to unauthorized access.

The algorithms that are best suited to this scenario are latent Dirichlet allocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learning methods that can discover abstract topics from a collection of text documents. LDA and NTM can provide a list of the top words for each topic, as well as the topic distribution for each document, which can help the auditors assess the relevance and priority of the topic12.

The other options are not suitable because:

Option B: A random forest classifier is a supervised learning method that can perform classification or regression tasks by using an ensemble of decision trees. A random forest classifier is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes3.

Option D: A linear support vector machine is a supervised learning method that can perform classification or regression tasks by using a linear function that separates the data into different classes. A linear support vector machine is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes4.

Option E: A linear regression is a supervised learning method that can perform regression tasks by using a linear function that models the relationship between a dependent variable and one or more independent variables. A linear regression is not suitable for discovering abstract topics from text documents, as it requires labeled data and a continuous output variable5.

1: Latent Dirichlet Allocation

2: Neural Topic Modeling

3: Random Forest Classifier

4: Linear Support Vector Machine

5: Linear Regression

The Recordio protobuf format is a binary data format that is optimized for training on SageMaker. It allows faster data loading and lower memory usage compared to other formats such as CSV or numpy arrays. The Recordio protobuf format also supports features such as sparse input, variable-length input, and label embedding. To use the Recordio protobuf format, the data needs to be serialized and deserialized using the appropriate libraries. Some of the built-in algorithms in SageMaker support the Recordio protobuf format as a content type for training and inference. References:

Common Data Formats for Training

Using RecordIO Format

Content Types Supported by Built-in Algorithms

Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data in real time using SQL queries. Amazon Random Cut Forest (RCF) is a SQL extension that enables anomaly detection on streaming data. RCF is an unsupervised machine learning algorithm that assigns an anomaly score to each data point based on how different it is from the rest of the data. A sliding window is a type of window that moves along with the data stream, so that the anomaly detection model can adapt to changing patterns over time. A tumbling window is a type of window that has a fixed size and does not overlap with other windows, so that the anomaly detection model is based on a fixed period of time. Therefore, option D is the best approach to meet the requirements of the question, as it uses RCF to calculate anomaly scores for each web traffic entry and uses a sliding window to adapt to changing web patterns over time.

Option A is incorrect because Amazon SageMaker Random Cut Forest (RCF) is a built-in model that can be used to train and deploy anomaly detection models on batch or streaming data, but it requires more steps and resources than using the RCF SQL extension in Amazon Kinesis Data Analytics. Option B is incorrect because Amazon SageMaker XGBoost is a built-in model that can be used for supervised learning tasks such as classification and regression, but not for unsupervised learning tasks such as anomaly detection. Option C is incorrect because k-Nearest Neighbors (kNN) is a SQL extension that can be used for classification and regression tasks on streaming data, but not for anomaly detection. Moreover, using a tumbling window would not allow the anomaly detection model to adapt to changing web patterns over time.

Using CloudWatch anomaly detection

Anomaly Detection With CloudWatch

Performing Real-time Anomaly Detection using AWS

What Is AWS Anomaly Detection? (And Is There A Better Option?)

 The IP Insights algorithm is designed to capture associations between entities and IP addresses, and can be used to identify anomalous IP usage patterns. The algorithm can learn from historical data that contains pairs of entities and IP addresses, and can return a score that indicates how likely the pair is to occur. The company can use this algorithm to train a model that can detect when a customer is accessing the site from a different location than usual, and request additional information accordingly. The company can also schedule updates and retraining of the model using new log data nightly to keep the model up to date with the latest IP usage patterns.

The other options are not suitable for this use case because:

Option A: The factorization machines (FM) algorithm is a general-purpose supervised learning algorithm that can be used for both classification and regression tasks. However, it is not optimized for capturing associations between entities and IP addresses, and would require labeling each record as either a successful or failed access attempt, which is a costly and time-consuming process.

Option C: The IP Insights algorithm is a good choice for this use case, but it does not require labeling each record as either a successful or failed access attempt. The algorithm is unsupervised and can learn from the historical data without labels. Labeling the data would be unnecessary and wasteful.

Option D: The Object2Vec algorithm is a general-purpose neural embedding algorithm that can learn low-dimensional dense embeddings of high-dimensional objects. However, it is not designed to capture associations between entities and IP addresses, and would require a different input format than the one provided by the company. The Object2Vec algorithm expects pairs of objects and their relationship labels or scores as inputs, while the company has data containing the source IP addresses and the login names of the requesting users.

IP Insights - Amazon SageMaker

Factorization Machines Algorithm - Amazon SageMaker

Object2Vec Algorithm - Amazon SageMaker

 The best way to address the overfitting problem in image classification is to increase the dropout rate at the flatten layer because the model is not generalized enough. Dropout is a regularization technique that randomly drops out some units from the neural network during training, reducing the co-adaptation of features and preventing overfitting. The flatten layer is the layer that converts the output of the convolutional layers into a one-dimensional vector that can be fed into the dense layers. Increasing the dropout rate at the flatten layer means that more features from the convolutional layers will be ignored, forcing the model to learn more robust and generalizable representations from the remaining features.

The other options are not correct for this scenario because:

Increasing the learning rate would not help with the overfitting problem, as it would make the optimization process more unstable and prone to overshooting the global minimum. A high learning rate can also cause the model to diverge or oscillate around the optimal solution, resulting in poor performance and accuracy.

Increasing the dimensionality of the dense layer next to the flatten layer would not help with the overfitting problem, as it would make the model more complex and increase the number of parameters to be learned. A more complex model can fit the training data better, but it can also memorize the noise and irrelevant details in the data, leading to overfitting and poor generalization.

Increasing the epoch number would not help with the overfitting problem, as it would make the model train longer and more likely to overfit the training data. A high epoch number can cause the model to converge to the global minimum, but it can also cause the model to over-optimize the training data and lose the ability to generalize to new data.

Dropout: A Simple Way to Prevent Neural Networks from Overfitting

How to Reduce Overfitting With Dropout Regularization in Keras

How to Control the Stability of Training Neural Networks With the Learning Rate

How to Choose the Number of Hidden Layers and Nodes in a Feedforward Neural Network?

How to decide the optimal number of epochs to train a neural network?