DASCA SDS Senior Data Scientist Exam Practice Test

Neural networks have been widely applied in various domains:

Option A (Character recognition): Correct application — neural networks are highly effective for OCR (Optical Character Recognition).

Option B (Stock market prediction): Correct application — neural networks are used to model time-series and nonlinear patterns in finance.

Option D (Image compression): Correct application — neural nets (autoencoders) are used for dimensionality reduction and compression.

Option C (Traveling salesman’s problem): NOT a typical neural network application. This is a combinatorial optimization problem usually solved with heuristics, dynamic programming, or optimization algorithms (not standard neural networks).

Thus, the correct answer is Option C (Traveling salesman’s problem).

Rehashing is a technique used in dynamic hash tables when the load factor (ratio of entries to bucket size) exceeds a certain threshold. It ensures efficient lookup, insertion, and deletion operations.

Option A (Correct): A larger hash table is allocated in memory to accommodate more entries.

Option B (Correct): A new hash function is typically required to map keys into the expanded table range.

Option C (Correct): All key-value pairs from the old table are re-inserted (rehashed) into the new table using the new hash function.

Since all three conditions (A, B, and C) are true, the best choice is Option E (All of the above).

Evolutionary algorithms are optimization techniques inspired by natural selection. They are often applied in neural network optimization for evolving architectures, weights, or hyperparameters.

Genetic Algorithm (Option A): Uses selection, crossover, and mutation to evolve neural network parameters.

Genetic Programming (Option B): Evolves computer programs or expressions; can be applied to evolve neural network structures.

Evolution Strategy (Option C): Focuses on optimization using mutation and adaptation of parameters.

Since all are used as evolutionary approaches in neural networks, the correct answer is Option D (All of the above).

The Big Data Business Model Maturity Index (BDBMMI) defines phases organizations pass through in leveraging data strategically:

Business Monitoring (A): Tracking metrics and reporting.

Business Insights (not listed in options but part of the framework).

Business Optimization (B): Using analytics to improve efficiency.

Data Monetization (D): Creating new revenue streams with data.

Business Metamorphosis (E): Transforming the business model through data.

Business Strategy (Option C): While strategy is essential, it is not one of the defined phases of BDBMMI.

Thus, the correct answer is Option C (Business Strategy).

The DevOps movement evolved as a natural extension of the Agile methodology.

Agile (Option A): Agile emphasizes iterative development, collaboration, and flexibility. While Agile improved software development speed, it created challenges in integrating development with IT operations. DevOps emerged to address this by bringing operations into the Agile cycle — enabling continuous integration, delivery, and deployment.

Waterfall (Option B): Incorrect. Waterfall is a rigid, sequential methodology, fundamentally opposite to the DevOps philosophy.

Promise-based algorithms (Option C): Not a methodology — irrelevant here.

Test-driven development and model-driven development (Option D): While these practices support DevOps, they are not the origin of the movement.

Thus, the DevOps movement is an outgrowth of Agile methodology.

Grid computing is a distributed computing model where resources (CPU, memory, storage) are pooled across multiple systems to solve large-scale problems.

Option A (Divide): Middleware helps allocate or divide resources dynamically to different tasks.

Option B (Combine): Middleware integrates diverse resources into a unified system, making them accessible for parallel computing.

Option C: Correct — middleware is the “glue” that enables both combining and dividing resources seamlessly across distributed nodes.

Option D: Incorrect.

Thus, grid computing middleware both combines and divides resources, making Option C correct.

The “Thinking Like a Data Scientist” (TLADS) decomposition process is a structured approach to align data science projects with business goals. It breaks complex business problems into smaller, analyzable parts:

Business Initiative (Option A): Defines the overarching organizational challenge or objective (e.g., reduce churn, increase revenue).

Business Stakeholder (Option B): Identifies decision-makers and end users whose requirements shape the use cases.

Strategic Nouns (Option C): Focuses on the entities (e.g., customer, product, supplier) that generate and consume data, serving as anchors for analytics design.

Since all three are valid elements of the TLADS decomposition, the correct answer is Option E (All of the above).

A burn down chart is a graphical representation used in Agile project management (including data science projects) to track progress. It typically plots time on the x-axis and work remaining on the y-axis.

Option A: Incorrect. Burn down charts don’t measure team “energy” or motivation levels.

Option B: Correct. The chart illustrates how much work remains versus how much has been completed, helping teams visualize progress toward goals. It helps identify whether the project is on track to finish within the sprint or deadline.

Option C: Incorrect. Hours worked after dark is irrelevant.

Option D: Incorrect. Budget reduction is not tracked in burn down charts.

Thus, the purpose of a burn down chart is to show the remaining work (tasks, story points, or features) decreasing over time. This provides transparency, supports stakeholder communication, and helps teams manage pace and velocity.

Python supports multiple libraries for scientific computing and data analysis, but the primary machine learning library is:

Scikit-learn (Option B): Provides a wide range of machine learning algorithms for classification, regression, clustering, model evaluation, and preprocessing. It is the core ML library in Python.

NumPy (Option A): Provides numerical computing and array operations, essential for ML but not a machine learning library itself.

Matplotlib (Option C): Used for data visualization.

SciPy (Option D): Supports scientific computing and numerical methods, not focused on ML models.

Therefore, the correct answer is Option B (Scikit-learn).

Business Intelligence (BI) is primarily focused on descriptive analytics and reporting — understanding historical and current business performance.

Option A (Descriptive analytics): Correct. BI uses dashboards, reports, and OLAP tools to summarize what has occurred in the past.

Option B ("What happened?"): Correct. BI answers retrospective questions by analyzing transactional and operational data.

Option C (Future state): Incorrect. Predicting future business outcomes falls under predictive analytics or advanced analytics, not BI.

Thus, the correct answer is Option D (Both A and B).

A Statement of Work (SOW) is a formal document that defines the scope, objectives, deliverables, timeline, and expectations of a project. In data science and IT projects, it ensures:

Clarity of scope (Option A): Everyone understands exactly what work should be done.

Clear priorities (Option B): It defines what is most critical for success.

Realistic expectations (Option C): It aligns stakeholders by setting measurable and achievable goals.

Since all of these are essential purposes of an SOW, the correct answer is Option D (All of the above).

Microservices architecture is a software design approach where applications are developed as a collection of small, loosely coupled, and independently deployable services.

Option A: Correct. Each microservice runs independently and has its own lifecycle, allowing scalability and modularity.

Option B: Correct. Each service can be treated as an independent project with its own repository, build pipeline, and deployment.

Option C: Correct. Microservices can be developed in different programming languages or frameworks, chosen based on the best fit for each specific requirement.

Therefore, the correct answer is Option D (All of the above).

Unsupervised learning is a machine learning approach where no labeled outputs are provided. The algorithm discovers patterns or structures directly from raw data.

Option A (Detecting regularities): Correct. Unsupervised learning identifies hidden structures such as clusters, associations, and dimensionality reductions (e.g., k-means clustering, PCA).

Option B (Detecting irregularities): Correct. Outlier detection is also a part of unsupervised learning, often used in anomaly detection (e.g., fraud detection, intrusion detection).

Option C: Correct, since unsupervised learning helps detect both regularities (clusters, groups) and irregularities (outliers, anomalies).

Thus, the correct answer is Option C (Both A and B).

Internal Process Optimization (IPO) is one of the core applications of data science in business operations. It focuses on improving internal efficiency, reducing costs, and enhancing productivity using data-driven insights.

Typical components of IPO include:

Business Monitoring (Option A): Tracking performance metrics and KPIs in real time.

Business Insights (Option C): Identifying trends, anomalies, and inefficiencies through analytics.

Business Optimization (Option D): Applying data models to optimize workflows, resource utilization, or supply chains.

However:

Business Metamorphosis (Option B): Refers to fundamental transformational change or reinvention of a business model, not process-level optimization. This is more aligned with strategic transformation, not internal process optimization.

Therefore, the correct answer is Option B (Business Metamorphosis).

ARIMA stands for AutoRegressive Integrated Moving Average, one of the most widely used models for time series forecasting.

AutoRegressive (AR): Model uses past values of the variable to predict future values.

Integrated (I): Differencing is applied to make the time series stationary.

Moving Average (MA): Model incorporates past forecast errors into predictions.

Option B: Correct — autoregressive + moving average is part of ARIMA’s name.

Options A, C, D: Incorrect because these terms are not recognized statistical modeling frameworks.

Option E: Incorrect, since only B is valid.

Thus, the correct answer is Option B.

TAR (Tape Archive) is a widely used archive file format in Unix/Linux environments. It is used to combine multiple files into a single archive file (with extension .tar).

Option A: Correct. TAR is specifically designed for archiving.

Option B (CSV): Incorrect. CSV (Comma-Separated Values) is a tabular text data format.

Option C (ARV): Incorrect — no such format.

Option D (Text): Incorrect. Though TAR may contain text files, the TAR format itself is not plain text but an archive format.

Option E: Incorrect since Option A is valid.

Thus, TAR is an Archive file format.

Exploratory analytics (often referred to as Exploratory Data Analysis – EDA) is a fundamental step in data science, enabling practitioners to discover initial insights, detect anomalies, and understand the structure of datasets before applying predictive or prescriptive modeling.

Option A (Understand the data content): Correct. EDA techniques (descriptive statistics, summary tables, profiling) reveal missing values, data types, and distributions.

Option B (Gain a high-level understanding of relationships): Correct. Correlation analysis, scatter plots, and cross-tabulations help identify dependencies between variables.

Option C (Understand patterns in the data): Correct. Visualization and clustering methods help discover hidden structures, seasonalities, and outliers.

Since exploratory algorithms contribute to all of these objectives, the correct answer is Option E (All of the above).

Customer Lifetime Value (CLTV) is a predictive metric estimating the total revenue a business can reasonably expect from a customer during their entire relationship.

Statement i: Correct. Many organizations calculate CLTV using historic transactional data, often looking at sales records over the past 12–18 months to establish baselines.

Statement ii: Correct. The primary purpose of CLTV is to help marketing, sales, and retail teams understand customer value, enabling them to allocate budgets effectively for retention, promotions, and personalized marketing.

Thus, both statements are correct → Option C (Both i and ii).

The Chief Data Monetization Officer (CDMO) is a C-level executive role emerging in data-driven organizations. Their focus is on turning data into business value and revenue growth.

Statement i: Correct. The CDMO is responsible for value realization from data and analytics investments.

Statement ii: Correct. The CDMO owns decisions around investment prioritization for data initiatives.

Statement iii: Correct. Since monetization is directly linked to business performance, the CDMO must be accountable for revenue and margin impact.

Hence, all three statements are correct, making the right answer Option C (All of the above).

Amortized performance refers to averaging the cost of operations over a sequence of actions, ensuring that while some operations may be costly, the overall average time per operation remains efficient.

Python Dictionaries (Option B): Implemented using hash tables. Insertions, deletions, and lookups typically run in O(1) average time, but occasionally require rehashing (costly). The high cost of rehashing is spread over many operations, giving amortized constant-time performance.

Option A (Hadoop dictionaries): Not standard terminology.

Option C (HDFS dictionaries): HDFS doesn’t use dictionary structures in this sense.

Option D (MapReduce dictionaries): MapReduce uses key-value pairs, but amortized dictionary performance is not its focus.

Thus, the correct answer is Option B (Python dictionaries).

Scrumban is a hybrid Agile methodology that merges Scrum and Kanban to take advantage of the strengths of both.

From Scrum, Scrumban adopts structured sprint planning, roles, and iterative review cycles.

From Kanban, it borrows the visual board system, continuous workflow management, and the pull-based approach, where tasks are pulled into the workflow only when capacity is available.

The pull-based system ensures that teams do not overload themselves and helps manage work-in-progress (WIP) effectively. This makes Scrumban particularly suitable for projects with frequent changes, ongoing maintenance tasks, or teams transitioning from Scrum to Kanban.

Thus, the correct answer is Option C.

Self-driving cars (autonomous vehicles) are an application of Reinforcement Learning (RL) in machine learning:

In RL, an agent (car) interacts with an environment (roads, obstacles, traffic) and learns to maximize rewards (e.g., safe driving, efficient navigation).

The system improves performance through trial-and-error learning, guided by reward signals such as staying in a lane or avoiding collisions.

Supervised learning (A): Used in some supporting tasks like image recognition (e.g., identifying stop signs), but not the core paradigm for self-driving.

Unsupervised learning (B): Useful for clustering sensor data, but again not the main paradigm.

Reinforcement learning (C): Correct, since self-driving fundamentally depends on RL decision-making.

Thus, the correct answer is Option C (Reinforcement Learning).

Optical Character Recognition (OCR) is the process of automatically recognizing and converting different types of documents — such as scanned paper documents, PDFs, or images — into editable and searchable text.

OCR systems use Machine Learning (ML) and Computer Vision techniques to detect and classify patterns of characters in images.

Algorithms like Convolutional Neural Networks (CNNs) are commonly used for image-based OCR.

While OCR may indirectly contribute to data mining or big data workflows, the core application is based on machine learning, where models are trained to classify and recognize text patterns.

Thus, OCR is primarily a Machine Learning application, making Option B correct.

Agile methodology is widely adopted in data science projects because these projects often involve uncertain goals, exploratory analysis, and changing requirements. Agile thrives in environments where iteration, collaboration, and adaptability are necessary.

Option A: True for Agile. If the final product is unclear (common in data science), Agile works well because it allows incremental discovery and iterative prototyping.

Option B: True for Agile. Agile frameworks (Scrum, Kanban) emphasize flexibility, which means the scope can evolve as stakeholders learn more from data and models.

Option C: True for Agile. Agile welcomes continuous changes through iterative sprints and feedback loops. This adaptability is crucial in machine learning model development where data insights often reshape project direction.

Since all three situations are valid for Agile, the correct answer to “Which is NOT correct?” is None of the above (Option D).

Time Series Analysis (TSA) is the process of analyzing data collected sequentially over time to extract meaningful insights.Applications include:

Option A: Correct. Event prediction (e.g., failure detection in IoT or predictive maintenance).

Option B: Correct. Forecasting future values (e.g., stock price, sales forecasting).

Option C: Correct. Pattern discovery in large-scale time series datasets using clustering, anomaly detection, or seasonality detection.

Since all three are true, the best answer is Option E (All of the above).