Amazon Web Services SAA-C03 AWS Certified Solutions Architect - Associate (SAA-C03) Exam Practice Test

Amazon QuickSight includes built-in ML-powered forecasting capabilities, allowing you to create, visualize, and interact with time series forecasts directly within the BI dashboard, with no ML experience or infrastructure management required. This is the lowest management overhead solution.

AWS Documentation Extract:

"Amazon QuickSight provides built-in ML-powered forecasting, allowing business users to forecast future trends with a few clicks and no machine learning expertise required."

(Source: Amazon QuickSight documentation)

A, C, D: Require additional setup, management, or ML knowledge.

For a large-scale multi-account AWS environment with many VPCs and centralized Direct Connect, AWS recommends using a Transit Gateway (TGW) architecture combined with a Direct Connect gateway (DXGW). This setup allows scalable, centralized connectivity between on-premises and multiple VPCs across accounts.

Step B: Creating a Direct Connect gateway and Transit Gateway in a central network account and connecting them via a transit VIF enables the on-premises network to access all connected VPCs.

Step D: Sharing the transit gateway with other accounts via AWS Resource Access Manager (RAM) allows the central TGW to attach VPCs in multiple accounts, simplifying multi-account connectivity.

Step F: To route cloud resources’ internet traffic back through the on-premises data center (for centralized egress), provisioning only private subnets and routing outbound internet traffic through NAT or firewall services in the data center is necessary. This requires configuring transit gateway and customer gateway routes appropriately.

Option A is partially correct in the use of Direct Connect gateway but association proposals are not scalable for hundreds of VPCs and accounts compared to transit gateway. Option C (internet gateway) is irrelevant here as traffic egress is required via on-premises data center, not directly to the internet. Option E (VPC peering) is not scalable for hundreds of VPCs.

To improve scalability and availability, EC2 Auto Scaling across multiple Availability Zones with an Application Load Balancer ensures resilient infrastructure. Migrating to Amazon Aurora MySQL with reader endpoints reduces read latency by offloading read traffic to replicas in otherAZs, while also increasing high availability.

Key Requirements:

Serverless architecture.

Handle traffic bursts with high availability.

Process orders asynchronouslyin the order they are received.

Analysis of Options:

Option A:Amazon SNS delivers messages to subscribers. However, SNS does not ensure ordering, making it unsuitable for FIFO (First In, First Out) requirements.

Option B:Amazon SQS FIFO queues support ordering and ensure messages are delivered exactly once. AWS Lambda functions can be triggered by SQS to process messages asynchronously and efficiently. This satisfies all requirements.

Option C:Amazon SQS standard queues do not guarantee message order and have "at-least-once" delivery, making them unsuitable for the FIFO requirement.

Option D:Similar to Option A, SNS does not ensure message ordering, and using AWS Batch adds complexity without directly addressing the requirements.

AWS References:

Amazon SQS FIFO Queues

AWS Lambda and SQS Integration

AWS Backup supports cross-Region backup for Amazon EFS, allowing automated, scheduled backups and replication to another Region. This managed service simplifies backup management and ensures data resilience without the need for custom scripts or manual processes

Comprehensive and Detailed Explanation:

Encryption at Rest: AWS Key Management Service (AWS KMS) provides centralized control over the cryptographic keys used to protect data. By using AWS KMS with Amazon S3, you can manage encryption keys and define policies to control access to them.

Encryption in Transit: By enforcing the aws:SecureTransport condition in the S3 bucket policy, you ensure that all data is transmitted over HTTPS, protecting data in transit.

Access Control: IAM policies allow you to define fine-grained permissions for users and roles, ensuring that only authorized entities can access the customer records.

Monitoring: AWS CloudTrail provides a record of actions taken by a user, role, or AWS service in Amazon S3, enabling you to monitor access to the records.

Comprehensive Explanation:Deploying the frontend as a CloudFront distribution with multiple origins provides an efficient and scalable solution. Using WAF rules with CloudFront protects against web vulnerabilities, while the multi-origin configuration allows traffic routing to the on-premises backend APIs. This approach minimizes operational overhead compared to managing EC2 instances.

AWS Global Accelerator reduces latency by directing users to the optimal Regional endpoint based on global network health and proximity. Amazon CloudFront caches static and dynamic content at edge locations for ultra-low latency access worldwide, improving performance and reducing server load.

The best practice for granting AWS resource access to EC2 instances is to use IAM roles, not users or long-lived access keys. You create an IAM role with a policy that grants the minimum permissions required, then attach that role to an instance profile associated with the EC2 instance. The instance then automatically receives temporary credentials for AWS service access.

AWS Documentation Extract:

"Attach an IAM role to your EC2 instances to securely grant permissions to AWS services according to the principle of least privilege. Never embed access keys in EC2 instances."

(Source: AWS EC2 documentation, IAM Roles for EC2)

A, B: Using IAM users and embedding keys violates security best practices.

C: IAM role credentials are automatically managed and never need to be manually attached as keys.

SAML 2.0-based federation allows organizations to integrate their on-premises Active Directory with AWS, enabling Single Sign-On (SSO) for AWS Management Console and AWS CLI/API access. This lets users continue using their existing AD credentials, removing the need to manage separate identities for AWS and on-premises systems. Mapping roles to AD groups provides granular access control and seamless management.

Reference Extract from AWS Documentation / Study Guide:

"AWS supports SAML 2.0 for federated access, enabling integration with Active Directory or other identity providers to provide single sign-on to AWS resources without creating separate IAM users."

Source: AWS Certified Solutions Architect – Official Study Guide, Identity and Access Management section.

A. EventBridge rule:Triggers an event whenever there is a change in CloudFront distribution, ensuring real-time monitoring.

B. ALB with WAF:Focuses on application-level security, not CloudFront logging.

C. Lambda + SNS:Provides notifications upon detection of changes in logging configuration.

D. GuardDuty:Monitors anomalies but does not specifically address CloudFront logging changes.

E. Private API + WAF:Irrelevant to CloudFront logging changes.

Amazon GuardDuty detects cryptocurrency mining and sends findings to Amazon EventBridge. You can use EventBridge to trigger an automated Lambda function to isolate EC2 instances (such as by removing security group access or stopping/isolating the instance).

AWS Documentation Extract:

"Amazon GuardDuty findings can be sent to Amazon EventBridge, which enables you to trigger an automated response using AWS Lambda."

(Source: AWS GuardDuty documentation)

B, C, D: Security Hub, Inspector, and Config are not directly used for this detection-to-isolation workflow.

Amazon CloudFront is a highly cost-effective CDN that caches content like images and videos at edge locations globally. This reduces latency and the load on the origin S3 bucket. It is ideal for static content that is accessed by many users.

The company’s requirements are:

Store gigabytes of rarely accessed files daily.

Files must be available within minutes during the first year.

Retain files for 7 years cost-effectively.

The S3 Glacier Instant Retrieval storage class provides low-cost, long-term storage for data accessed occasionally, with millisecond retrieval time. This fits the requirement for availability within minutes during the first year. After one year, transitioning files to S3 Glacier Deep Archive (the lowest cost S3 storage class) with longer retrieval times is cost-effective for data retention over 7 years.

Option B uses S3 Standard and Glacier Flexible Retrieval, which is higher cost during the first year and slower retrieval times. Option C is less cost-efficient because EBS volumes are expensive for rarely accessed data and snapshots incur additional costs. Option D uses EFS, which is designed for low-latency file storage but is more expensive than S3 Glacier classes for long-term archival.

If the SQS visibility timeout is set shorter than the time it takes for the application to process and delete the message, the message becomes visible to other consumers and can be processed again, resulting in duplicate processing. This is a common cause of duplicate messages when using SQS.

Reference Extract from AWS Documentation / Study Guide:

"If the visibility timeout for a message is set shorter than the time it takes to process the message, the message becomes visible again and can be received and processed again, resulting in duplicate processing."

Source: AWS Certified Solutions Architect – Official Study Guide, SQS and Messaging section.

Converting the existing DynamoDB table to aglobal tableprovides active-active replication and low-latency reads and writes in both Regions. DynamoDB global tables are specifically designed for multi-Region and multi-active use cases.

Option A:GSIs do not provide multi-Region replication or active-active capabilities.

Option B and D:Using DynamoDB Streams and custom replication is less operationally efficient than global tables and introduces additional complexity.

AWS Documentation References:

DynamoDB Global Tables

Comprehensive and Detailed Explanation:

To handle increased loads effectively, it's essential to implement Auto Scaling for both frontend and backend tiers:

Frontend Auto Scaling Group: Scaling based on incoming network traffic ensures that the application can handle increased user requests.

Backend Auto Scaling Group: Scaling based on the Amazon SQS queue backlog ensures that the backend can process messages as they arrive, preventing delays.

This approach allows each tier to scale independently based on its specific load, ensuring optimal resource utilization and performance.

Amazon S3 Access Points simplify managing data access for shared datasets in S3 by allowing the creation of distinct access policies for different applications or users. Each access point has its own policy and can be managed independently. This method avoids overloading a single bucket policy and helps remain within policy size limits.

Option D provides a scalable and operationally efficient solution by offloading individual access controls from a central bucket policy to individually managed access points, which is ideal for environments with many consuming applications.

Option A:The ALB must accept HTTPS traffic from the public internet. Allowing inbound traffic on port 443 from 0.0.0.0/0 enables this functionality.

Option C:The ALB must forward HTTPS traffic to the web application servers on port 443. Outbound traffic for port 443 must be allowed for this communication.

Option E:The ALB must perform health checks on the web application servers over HTTPS on port 8443. Outbound traffic for port 8443 must be allowed for this purpose.

Option B:Allowing all outbound traffic is overly permissive and does not align with the specific requirements.

Option D and F:Inbound traffic to the ALB from the web application instances is unnecessary because the flow of traffic is from the ALB to the web application instances, not vice versa.

AWS Documentation References:

Application Load Balancer Security Groups

Health Checks for ALBs

Amazon S3 File Gateway provides a local NFS mount point, which can be used with minimal changes by the legacy application. Files are transparently uploaded to Amazon S3, allowing the web application to access them directly from S3. This is the most cost-effective and operationally simple way to bridge legacy on-premises NFS output with S3, requiring no changes to the batch process.

AWS Documentation Extract:

“With S3 File Gateway, you can provide applications a local file interface to Amazon S3. S3 File Gateway presents a file-based interface (NFS or SMB), allowing you to use S3 as your scalable, durable storage while making files available to legacy applications.”

(Source: AWS Storage Gateway documentation)

A: Outposts is far more costly and complex than needed.

B: Volume Gateway presents iSCSI block storage, not NFS.

C: Requires re-coding the legacy app to use S3 APIs, not NFS.

A. On-premises tool via internet:Incurs high costs due to large data transfers over the internet.

B. AWS Region tool via internet:Does not utilize Direct Connect, leading to potential latency and higher costs.

C. On-premises tool via Direct Connect:Adds latency for querying and visualization.

D. AWS Region tool via Direct Connect:Reduces latency and leverages Direct Connect for optimized data transfer costs.

Comprehensive and Detailed Explanation:

For a MySQL database experiencing high write operations, using Amazon RDS with Provisioned IOPS (io1 or io2) SSD storage is recommended to achieve consistent and low-latency performance. Provisioned IOPS allows you to specify a desired IOPS rate, which is crucial for write-intensive workloads.

Monitoring write operation metrics through Amazon CloudWatch enables you to observe performance and adjust the provisioned IOPS as needed to meet application demands.

AWS Backup is a fully managed backup service that can create backup plans for EC2 instances, including both instance configurations and attached EBS volumes, with scheduled and cross-Region copy capabilities. By adding the EC2 instances to the resource assignment in the backup plan, AWS Backup automatically backs up all configurations and attached EBS volumes, and can copy backups to a secondary Region for disaster recovery, providing the highest operational efficiency with the least manual effort.

AWS Documentation Extract:

“AWS Backup provides fully managed backup for EC2 instances and attached EBS volumes, with scheduling, retention, and cross-Region copy built in. By adding the EC2 instance as a resource, the backup includes both configuration and attached volumes.”

(Source: AWS Backup documentation)

A, D: Custom Lambda scripts increase operational overhead and are not as integrated or robust as AWS Backup.

C: Assigning only EBS volumes does not include the EC2 instance configuration, which is needed for full recovery.

AWS PrivateLink enables private connectivity between VPCs and supported AWS or third-party services without exposing traffic to the public internet. It ensures secure and private communications, making it ideal for connecting internal services to SaaS applications hosted in AWS.

Amazon SQS provides durable, decoupled message storage for distributed systems. Using SQS as a job queue enables each EC2 instance to process a message independently. Scaling the Auto Scaling group based on the SQS queue length ensures parallelism and elasticity, aligning compute resources with workload volume.

AmazonKinesis Data Streamsis the best choice for this scenario:

Message throughput: Kinesis Data Streams supports high throughput with enhanced fan-out and dedicated throughput for consumers.

Large message size: Supports message sizes up to 1 MB, meeting the 500 KB requirement.

Message retention: Data streams can retain messages for up to 365 days.

Strict ordering: Guarantees message ordering within shards.

Why Other Options Are Not Ideal:

Option B:

While SQS FIFO supports strict ordering, SNS topics do not. SNS also does not natively support message retention or strict ordering across consumers.Does not meet requirements.

Option C:

EventBridge does not provide strict ordering guarantees or message retention beyond 24 hours.Does not meet requirements.

Option D:

SNS topics with Data Firehose are not designed for use cases requiring strict ordering or long message retention.Does not meet requirements.

AWS References:

Amazon Kinesis Data Streams:AWS Documentation - Kinesis Data Streams

AWS Messaging Services Comparison:AWS Documentation - Messaging Services

The requirements specify capturing unpredictable and sudden spikes in customer activity, integrating easily with other web applications, and including authorization.

Amazon API Gateway with Lambda authorizer provides a secure, scalable entry point with flexible authorization mechanisms including token validation.

Amazon Kinesis Data Firehose is a fully managed service to reliably load streaming data into destinations such as Amazon S3, which fits well for capturing streaming customer activity data.

API Gateway integrates natively with Firehose for direct ingestion.

This combination supports unpredictable traffic, smooth scaling, and simple authorization.

Option B uses Kinesis Data Streams, which requires more management than Firehose and is less optimized for direct API integration. Options A and D use Gateway Load Balancer and ECS containers plus EFS, which add complexity and are less suited for unpredictable traffic with integrated authorization.

The requirement is to provide granular, scalable access to thousands of tables and columns in a data lake across many users and departments, with the least operational overhead.

AWS Lake Formation supports tag-based access control (TBAC) using LF-tags (Lake Formation tags), which allows you to assign tags to tables, columns, and databases. You can then define permissions on resources by specifying tags rather than managing permissions for individual resources. This approach is highly scalable and efficient when dealing with a growing number of tables and columns. By associating IAM roles to departments and granting access based on LF-tags, you dramatically reduce the operational burden as new tables or columns are added; you only need to assign the appropriate tags.

Amazon Athena can directly query data in S3 with Lake Formation providing fine-grained access control.

AWS Documentation Extract:

"With LF-tag-based access control, you can grant permissions to resources based on tags, making it easy to manage access at scale, especially in environments with large and dynamic numbers of resources."

"LF-tags provide a scalable way to manage permissions for large numbers of resources without having to define permissions individually for each table or column."

(Source: AWS Lake Formation documentation, Access Control, Tag-Based Access Control)

Other options:

A: Would require managing explicit permissions for each table and column as the environment grows, increasing operational overhead.

B & D: Involve significant duplication of resources (clusters) and do not scale as efficiently as a centralized data lake with tag-based access.

Amazon DocumentDB global clusters support managed cross-region failover, allowing you to promote a secondary region to become the new primary with minimal downtime. This ensures business continuity during maintenance or regional disruptions.

Reference Extract:

"Amazon DocumentDB global clusters support managed cross-Region failover, allowing you to recover quickly from regional disruptions with minimal downtime."

Source: AWS Certified Solutions Architect – Official Study Guide, DocumentDB and Resiliency section.

AWS Lake Formationis designed for managing fine-grained access control to data in an efficient manner:

Granular Permissions: Lake Formation allows column-level, row-level, and table-level access controls, which can precisely define access to PII data.

Integration with AWS Glue Catalog: Lake Formation natively integrates with AWS Glue for seamless data cataloging and access control.

Operational Efficiency: Centralized access control policies minimize the need for separate IAM roles or policies.

Why Other Options Are Not Ideal:

Option A:

Creating multiple IAM policies introduces complexity and lacks column-level access control.Not efficient.

Option B:

Managing multiple IAM roles for granular access is operationally complex.Not efficient.

Option D:

Creating views in Glue adds unnecessary complexity and may not provide the level of granularity that Lake Formation offers.Not the best choice.

AWS References:

AWS Lake Formation:AWS Documentation - Lake Formation

Fine-Grained Permissions with Lake Formation:AWS Documentation - Fine-Grained Permissions

Correct Approach:

AWS Security Groups:

Security groups operate at the instance level, making them the ideal tool for controlling access to specific resources such as an Amazon RDS database.

By default, security groups deny all incoming traffic. You can allow access by explicitly specifying another security group.

Associating an RDS database security group with the EC2 instances' security group ensures only the specified EC2 instances can access the RDS database.

Incorrect Options Analysis:

Option A: Using CIDR blocks for IP-based access is less secure and more difficult to manage. Additionally, Auto Scaling groups dynamically allocate IP addresses, making this approach impractical.

Option B: Network ACLs (NACLs) operate at the subnet level and are stateless. While NACLs can deny or allow traffic, they are not suited to application-specific access control.

Option D: Similar to Option B, using a NACL with CIDR ranges for EC2 IPs is difficult to manage and not application-specific.

Key Requirements:

Handle traffic spikes efficiently and reduce latency caused by cold starts.

Optimize costs during low traffic periods.

Analysis of Options:

Option A:

Provisioned Concurrency:Reduces cold start latency by pre-warming Lambda environments for the required number of concurrent executions.

AWS Application Auto Scaling:Automatically adjusts provisioned concurrency based on demand, ensuring cost optimization by scaling down during low traffic.

Correct Approach:Provides a balance between performance during traffic spikes and cost optimization during idle periods.

Option B:

Using EC2 instances with Auto Scaling introduces unnecessary complexity for a serverless architecture. It requires additional management and does not address the issue of cold starts for Lambda.

Incorrect Approach:Contradicts the serverless design philosophy and increases operational overhead.

Option C:

Setting a fixed concurrency level ensures performance during spikes but does not optimize costs during low traffic. This approach would maintain provisioned instances unnecessarily.

Incorrect Approach:Lacks cost optimization.

Option D:

Using EventBridge Scheduler for periodic invocations may reduce cold starts but does not dynamically scale based on traffic demand. It also leads to unnecessary invocations during idle times.

Incorrect Approach:Suboptimal for high traffic fluctuations and cost control.

AWS Solution Architect References:

AWS Lambda Provisioned Concurrency

AWS Application Auto Scaling with Lambda

AWS Config is a service designed to assess, audit, and evaluate the configurations of AWS resources. It continuously monitors and records your AWS resource configurations and allows you to automate the evaluation of recorded configurations against desired configurations. By starting a configuration recorder, AWS Config will capture changes to supported resource types as configuration items—without the need to modify any of the existing resources. This provides a full history of configuration changes and is specifically intended for exactly this use case.

AWS Documentation Extract:

“AWS Config provides a detailed view of the configuration of AWS resources in your AWS account. This includes how the resources are related to one another and how they were configured in the past so you can see how the configurations and relationships change over time.”

“You can start the configuration recorder, which will record the configuration changes of the supported resources in your AWS account.”

(Source: AWS Config documentation, What is AWS Config?)

Other options:

B: CloudFormation drift detection only works for resources created and managed by CloudFormation and requires stacks.

C: Amazon Detective is used for analyzing and investigating security findings, not for resource configuration tracking.

D: AWS Audit Manager is used for automating evidence collection to help with audits, not for tracking resource configurations.

The ALB must be in a public subnet to receive internet traffic. The EC2 instances and the RDS database should be in private subnets to prevent direct internet access, minimizing the attack surface. This aligns with AWS security best practices for web application architectures.

Reference Extract:

"Internet-facing ALBs should be placed in public subnets; EC2 instances and RDS databases should be in private subnets to restrict direct internet access."

Source: AWS Certified Solutions Architect – Official Study Guide, Network Security and Design section.

Comprehensive and Detailed Explanation:

To ensure that log files are immutable and cannot be deleted or overwritten for a specified retention period, Amazon S3 offers Object Lock in Compliance Mode. When enabled:

Compliance Mode: Ensures that a protected object version can't be overwritten or deleted by any user, including the root user in your AWS account, for the duration of the retention period. AWS Documentation

S3 Versioning: Must be enabled on the bucket to use Object Lock. This allows multiple versions of an object to be stored, ensuring that previous versions are preserved and protected. Amazon Web Services, Inc.

By enabling S3 Versioning and applying Object Lock in Compliance Mode with a 1-year retention period, the company can meet regulatory requirements to retain log data securely and prevent any modifications or deletions during that time.

By exporting AWS Cost and Usage Reports (CUR) to Amazon S3 and analyzing them with Amazon QuickSight, companies can generate interactive visual dashboards. This solution is fully AWS-managed, requires no third-party tools, and integrates deeply with AWS cost data.

The SELECT INTO OUTFILE S3 feature allows you to export Amazon Aurora MySQL data directly to Amazon S3 with minimal operational overhead. This method is efficient and cost-effective for archiving historical data.

You can configure S3 Lifecycle rules to transition the exported data to lower-cost storage (e.g., S3 Glacier or S3 Standard-IA) and eventually delete it after 5 years.

No need for additional ETL tools like Glue or DataBrew unless complex transformations are required.

Provisioned IOPS SSD (io1 or io2) is designed for I/O-intensive workloads that require low latency and consistent throughput, which is critical for transactional and production databases. It provides predictable performance, unlike General Purpose SSD, which is burst-based.

When using imported key material with AWS KMS, you maintain control over the key lifecycle. AWS KMS allows you to import new key material into an existing KMS key (of type "external" or "imported"), thus rotating the key material without changing the key ID or ARNs. This enables applications to continue using the same key for cryptographic operations without disruption.

You can also set an expiration time for the old key material, after which AWS KMS deletes the old material and requires new key material to be imported, enforcing regular rotation per your compliance requirements.

AWS Documentation Extract:

"To rotate imported key material, you can re-import new key material into the same KMS key. This retains the same key ID and ARNs so applications are unaffected. You can set an expiration time for imported key material and replace it as needed, ensuring compliance with your rotation policy."

(Source: AWS Key Management Service Developer Guide, Importing Key Material, Rotating Key Material)

Other options:

A: You cannot create a new KMS key with the same key ID as an existing one.

B: Deleting and recreating the key disrupts application access because the key ID changes.

D: Automatic rotation is only available for AWS-managed keys, not for imported key material.

AWS Outposts brings native AWS services (including Amazon EMR) on-premises, ideal when data residency or latency constraints require local processing.

You benefit from AWS’s managed services while meeting the requirement to keep data processing local.

Provisioned IOPS SSD (io1 or io2) volumes are designed to deliver predictable, high performance for I/O-intensive workloads such as databases. They offer consistent, low-latency performance and durability, making them ideal for business-critical applications that cannot tolerate latency or data loss.

The best solution is to useAmazon SQSto receive updates from the mobile app and process them with anAWS Lambdafunction. The Lambda function can rewrite the message body as necessary for each shipper and then publish the updates to the appropriateSNS topicfor distribution. Thissetup ensures that each shipper receives only the relevant data and minimizes operational overhead by using managed services.

Option A (SNS filter policy): SNS does not have the capability to rewrite message bodies before forwarding.

Option C (Second SNS topic): Using an additional SNS topic adds unnecessary complexity without solving the message rewriting requirement.

Option D (EventBridge Pipes): EventBridge Pipes is more complex than necessary for this use case, and Lambda can handle the logic more efficiently.

AWS References:

Amazon SQS

Amazon SNS with Lambda

AWSDirect Connectis the best solution for establishing a consistent, low-latency connection from an on-premises data center to AWS without using the public internet. Direct Connect offers dedicated, high-throughput, and low-latency network connections, which are ideal for performance-sensitive applications like a trading platform that processes high volumes of market data and stock trades.

Direct Connect provides a private connection to your AWS VPC, ensuring that data doesn't traverse the public internet, which enhances both security and performance consistency.

AWS References:

AWS Direct Connectprovides a dedicated network connection to AWS services with consistent, low-latency performance.

Best Practices for High Performance on AWSfor performance-sensitive workloads like trading platforms.

Why the other options are incorrect:

A. AWS Client VPN: While this offers secure connectivity, it's over the public internet and is not designed for the low-latency, high-performance needs of a trading platform.

C. AWS PrivateLink: PrivateLink is used for connecting VPCs and services within AWS, but it is not designed for connecting on-premises data centers to AWS.

D. AWS Site-to-Site VPN: Although this provides secure connectivity, it uses the public internet, which can introduce latency and doesn't meet the low-latency requirements of the use case.

This solution meets the company's requirements with minimal administrative overhead and ensures security and ease of management.

AWS AppConfig: AWS AppConfig is a service designed to manage application configuration in a secure and validated way. It allows you to deploy configurations safely and quickly without affecting the application's performance or availability.

AWS Secrets Manager: AWS Secrets Manager is specifically designed to manage, retrieve, and rotate credentials for databases and other services. It integrates seamlessly with AWS services like Amazon RDS, making it an ideal solution for securely storing and retrieving database credentials. Secrets Manager also provides automatic rotation of credentials, reducing the operational burden.

Why Not Other Options?:

Option B (AWS Lambda + Parameter Store): While AWS Lambda can be used for managing configurations and AWS Systems Manager Parameter Store can store credentials, this approach involves more manual setup and does not offer the same level of integrated management and security as AppConfig and Secrets Manager.

Option C (Encrypted S3 Configuration File): Storing configuration and credentials in S3 files involves more manual management and security considerations, increasing the administrative overhead.

Option D (AppConfig + RDS for credentials): RDS is not designed for storing application credentials; it's better suited for managing database instances and their configurations.

AWS References:

AWS AppConfig- Describes how to use AWS AppConfig for managing application configurations.

AWS Secrets Manager- Provides details on securely storing and retrieving credentials using AWS Secrets Manager.

For improving availability and performance of the hybrid application, the following solutions are optimal:

AWS Global Accelerator (Option A): Global Accelerator provides high availability and improves performance by using the AWS global network to route user traffic to the nearest healthy endpoint (across AWS Regions). By adding the Network Load Balancers as endpoints, Global Accelerator ensures that traffic is routed efficiently to the closest endpoint, improving both availability and performance.

Network Load Balancer (Option D): Thestateful TCP-based workloadhosted on Amazon EC2 instances and thestateless UDP-based workloadhosted on-premises are best served by Network Load Balancers (NLBs). NLBs are designed to handle TCP and UDP traffic with ultra-low latency and can route traffic to both EC2 and on-premises endpoints.

Option B (CloudFront and Route 53): CloudFront is better suited for HTTP/HTTPS workloads, not for TCP/UDP-based applications.

Option C (ALB): Application Load Balancers do not support the stateless UDP-based workload, making NLBs the better choice for both TCP and UDP.

AWS References:

AWS Global Accelerator

Network Load Balancer

Amazon Athena provides a cost-effective, serverless way to query data without affecting the performance of DynamoDB. By using the Athena DynamoDB connector, the company can perform the necessary SQL queries without consuming read capacity on the DynamoDB table, which is essential for minimizing impact on provisioned throughput.

Key benefits:

Minimal Impact on Provisioned Capacity: Athena queries do not directly impact DynamoDB’s read capacity, making it ideal for running analytics without affecting the customer-facing workloads.

Cost-Effective: Athena is a serverless solution, meaning you pay only for the queries you run, making it highly cost-effective compared to running a dedicated cluster like Amazon EMR or Redshift.

AWS Documentation: The use of Athena to query DynamoDB through its connector aligns with AWS's best practices for performance efficiency and cost optimization​​.

Amazon S3 Storage Lens:

S3 Storage Lens provides organization-wide visibility into object storage usage and activity trends.

It can generate metrics and insights about your S3 buckets, including versioning status.

Configuration:

Enable S3 Storage Lens at the organization level.

Configure the dashboard to include the versioning status metric.

Identify Non-Versioned Buckets:

Use the S3 Storage Lens dashboard to filter and identify buckets that do not have versioning enabled.

Storage Lens provides detailed insights and reports which can be used to enforce compliance and manage storage effectively.

Operational Efficiency: Using S3 Storage Lens provides a centralized, easy-to-use interface for monitoring bucket configurations across multiple Regions and accounts, reducing the need for custom scripts or manual checks.

For the migration of data from an on-premises Oracle database to Amazon Aurora PostgreSQL, this solution effectively handles schema conversion, data migration, and ongoing data replication.

AWS Schema Conversion Tool (SCT): SCT is used to convert the Oracle database schema to a format compatible with Aurora PostgreSQL. This tool automatically converts the database schema and code objects, like stored procedures, to the target database engine.

AWS Database Migration Service (DMS): DMS is employed to perform the data migration. It supports both full-load migrations (for initial data transfer) and continuous replication of ongoing changes (Change Data Capture, or CDC). This ensures that any updates to the Oracle database during the migration are captured and applied to the Aurora PostgreSQL database, minimizing downtime.

Why Not Other Options?:

Option A (SCT + DMS full-load only): This option does not capture ongoing changes, which is crucial for a live database migration to ensure data consistency.

Option B (DataSync + S3): AWS DataSync is more suited for file transfers rather than database migrations, and it doesn’t support ongoing change replication.

Option D (Snowball + S3): Snowball is typically used for large-scale data transfers that don’t require continuous synchronization, making it less suitable for this scenario where ongoing changes must be captured.

AWS References:

AWS Schema Conversion Tool- Guidance on using SCT for database schema conversions.

AWS Database Migration Service- Detailed documentation on using DMS for data migrations and ongoing replication.

To ensure high availability and scalability, the web application should run in anAuto Scaling groupacross two Availability Zones behind anApplication Load Balancer (ALB). The database should be migrated toAmazon RDSwithMulti-AZ deployment, which ensures fault tolerance and automatic failover in case of an AZ failure. This setup minimizes administrative overhead while meeting the company's requirements for high availability and scalability.

Option A: Read replicas are typically used for scaling read operations, and Multi-AZ provides better availability for a transactional database.

Option B: Replicating across AWS Regions adds unnecessary complexity for a single web application.

Option D: EC2 instances across three Availability Zones add unnecessary complexity for this scenario.

AWS References:

Auto Scaling Groups

Amazon RDS Multi-AZ

Amazon EFSwithMax I/O performance modeis designed for workloads that require high levels of parallelism, such as video processing across multiple EC2 instances. EFS provides shared file storage that can be mounted on both Windows and Linux EC2 instances, and the Max I/O mode ensures the best performance for handling large files and concurrent access across multiple instances.

Option A and B (FSx for Windows File Server): FSx for Windows File Server is optimized for Windows workloads and would not be ideal for Linux instances or high-throughput, parallel workloads.

Option D (EFS General Purpose mode): General Purpose mode offers lower latency but doesn't support the high throughput needed for large, concurrent workloads.

AWS References:

Amazon EFS Performance Modes

A. Kinesis Data Streams:Supports high throughput, strict ordering, multiple consumers, and data retention for 365 days.

B. SNS + SQS FIFO:Can enforce ordering but lacks native support for 500 KB messages and retention requirements.

C. EventBridge:Lacks strict ordering and message size compatibility.

D. SNS + Firehose:Not designed for strict ordering or large message sizes.

EBS Encryption:

Default EBS Encryption: Can be enabled for new EBS volumes.

Use of AWS KMS: Specify AWS KMS keys to handle encryption and decryption of data transparently.

Amazon RDS Encryption:

RDS Encryption: Encrypts the underlying storage for RDS instances using AWS KMS.

Configuration: Enable encryption when creating the RDS instance or modify an existing instance to enable encryption.

Least Amount of Changes:

Both EBS and RDS support seamless encryption with AWS KMS, requiring minimal changes to the existing infrastructure.

Enables compliance with regulatory requirements without modifying the application.

Operational Efficiency: Using AWS KMS for both EBS and RDS ensures a consistent, managed approach to encryption, simplifying key management and enhancing security.

Amazon API Gatewayprovides a scalable and reusable solution for interacting with DynamoDB without requiring direct access by developers. By setting up a REST API with a POST methodthat integrates with DynamoDB'sPutItemaction, developers can submit data (such as user ratings) to the DynamoDB table through API Gateway, without having to directly interact with the database. This solution is serverless and minimizes operational overhead.

Option A: Using ALB with Lambda adds complexity and is less efficient for this use case.

Option B: While using Lambda is possible, API Gateway provides a more scalable, reusable interface.

Option C: SQS with Lambda introduces unnecessary components for a simple put operation.

AWS References:

Amazon API Gateway with DynamoDB

This solution provides encryption at rest and in transit with the least operational overhead while adhering to the company’s security policies.

Encryption at Rest: Amazon RDS for MySQL can be configured to encrypt data at rest by using AWS Key Management Service (KMS) managed keys. This encryption is applied automatically to all data stored on disk, including backups, read replicas, and snapshots. This solution requires minimal operational overhead because AWS manages the encryption and key management process.

Encryption in Transit: AWS Certificate Manager (ACM) allows you to provision, manage, and deploy SSL/TLS certificates seamlessly. These certificates can be used to encrypt data in transit by configuring the MySQL instance to use SSL/TLS for connections. This setup ensures thatdata is encrypted between the application and the database, protecting it from interception during transmission.

Why Not Other Options?:

Option B (IPsec tunnels): While IPsec tunnels encrypt data in transit, they are more complex to manage and require additional configuration and maintenance, leading to higher operational overhead.

Option C (Third-party application-level encryption): Implementing application-level encryption adds complexity, requires code changes, and increases operational overhead.

Option D (VPN for encryption): A VPN solution for encrypting data in transit is unnecessary and adds additional complexity without providing any benefit over SSL/TLS, which is simpler to implement and manage.

AWS References:

Amazon RDS Encryption- Information on how to configure and use encryption for Amazon RDS.

AWS Certificate Manager (ACM)- Details on using ACM to manage SSL/TLS certificates for securing data in transit.

The best solution to enforce encryption at rest for Amazon EBS volumes is to use anIAM policyto restrict the creation of unencrypted volumes. To automatically identify and remediate unencryptedvolumes, you can useAWS Configrules, which continuously monitor the compliance of resources, andAWS Systems Managerto automate the remediation by encrypting existing unencrypted volumes. This setup requires minimal administrative overhead while ensuring compliance.

Option B (KMS): KMS is for managing encryption keys, but Config and Systems Manager provide a better solution for automatic detection and enforcement.

Option C (Macie): Macie is for data classification and is not suitable for this use case.

Option D (Inspector): Inspector is used for security vulnerabilities, not encryption compliance.

AWS References:

AWS Config Rules

AWS Systems Manager

The company is looking for a solution that provides single sign-on (SSO) across multiple AWS accounts while continuing to manage users and groups in their on-premises Active Directory (AD). AWS IAM Identity Center (formerly AWS SSO) is the recommended solution for this type of requirement.

AWS IAM Identity Centerprovides a centralized identity management solution, enabling single sign-on across multiple AWS accounts and other cloud applications. It can integrate with on-premises Active Directory to leverage existing users and groups.

By configuring a two-way forest trust relationship between AWS Directory Service for Microsoft Active Directory and the company's on-premises Active Directory, users can be authenticated by their on-premises AD and still access AWS resources through IAM Identity Center. This solution allows centralized management of AWS accounts within AWS Organizations.

The two-way trust allows mutual access between the on-premises AD and the AWS Directory Service. This means that users and groups in the on-premises AD can be used for authentication in AWS IAM Identity Center while maintaining the existing identity management system.

AWS References:

AWS IAM Identity Center Documentation

AWS Directory Service for Microsoft Active Directory Trust Relationships

AWS Directory Service Integration with IAM Identity Center

Why the other options are incorrect:

A. Create an Enterprise Edition Active Directory in AWS Directory Service: This would require setting up a new directory and managing it in AWS, which adds unnecessary overhead. The requirement is to continue using the existing on-premises AD, making this option unsuitable.

C. Use AWS Directory Service and create a two-way trust relationship: While this approach establishes a trust between on-premises AD and AWS Directory Service, it does not address the single sign-on (SSO) requirements across multiple AWS accounts through IAM Identity Center.

D. Deploy an identity provider (IdP) on Amazon EC2: This is more complex than necessary and introduces more management overhead. AWS IAM Identity Center natively supports integration with on-premises Active Directory without requiring a custom IdP.

In Amazon Aurora, a custom endpoint is a feature that allows you to create a load-balanced endpoint that directs traffic to a specific set of instances in your Aurora DB cluster. This is particularly useful when you want to route traffic to a subset of instances that have different configurations or when you want to isolate specific workloads (e.g., reporting queries) to certain instances.

Custom Endpoint: The correct solution is to create a custom endpoint that includes the three Aurora Replicas that the department wants to use for near-real-time reporting. This custom endpoint will distribute the reporting queries only across the three selected replicas with the specified compute and memory configurations, ensuring that these queries do not affect the rest of the DB cluster.

Other Options:

Option B (Create a three-node cluster clone): This would create a separate cluster with its own resources, but it is not necessary and could incur additional costs. Also, it doesn't leverage the existing replicas.

Option C (Use any of the instance endpoints): This would involve manually managing connections to individual instances, which is not scalable or automatic.

Option D (Use the reader endpoint): The reader endpoint would distribute the read queries across all replicas in the cluster, not just the selected three. This would not meet the requirement to limit the reporting queries to only three specific replicas.

AWS References:

Amazon Aurora Endpoints- Provides detailed information on the different types of endpoints available in Aurora, including custom endpoints.

Custom Endpoints in Amazon Aurora- Specific documentation on how to create and use custom endpoints to direct traffic to selected instances in an Aurora cluster.

AWS Glue Crawler: AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to prepare and load data for analytics. A Glue crawler can automatically discover new data and schema in Amazon S3, making it easy to keep the data catalog up-to-date.

Crawling the Data:

Set up an AWS Glue crawler to scan the S3 bucket containing the clickstream data.

The crawler will automatically detect the schema and create/update the tables in the AWS Glue Data Catalog.

Amazon Athena:

Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.

Once the data catalog is updated by the Glue crawler, use Athena to query the clickstream data directly in S3.

Operational Efficiency: This solution leverages fully managed services, reducing operational overhead. Glue crawlers automate data cataloging, and Athena provides a serverless, pay-per-query model for quick data analysis without the need to set up or manage infrastructure.

AmazonEventBridgeAPI destinations allow you to send data from AWS to external systems, like Salesforce, using HTTP APIs, including those secured with OAuth. This provides a secure and scalable solution for sending messages from the order processing application to Salesforce.

Option A and B (SNS): SNS is not ideal for OAuth-secured external APIs and lacks the necessary OAuth integration.

Option D (MSK): Amazon MSK is a Kafka-based streaming solution, which is overkill for simple message forwarding to Salesforce.

AWS References:

Amazon EventBridge API Destinations

Amazon Elastic File System (EFS) is the ideal solution for migrating legacy applications that require NFS (Network File System) communication. EFS provides fully managed, scalable NFS storage in the cloud, and it supports the standard NFS protocols, allowing the legacy application to continue using NFS without modification after migration to AWS.

Key AWS features:

NFS Support: EFS natively supports the NFSv4 protocol, which makes it the best solution for workloads that rely on NFS communication.

Scalability and Availability: EFS automatically scales as application demands grow, making it a highly available and reliable storage solution.

AWS Documentation: According to AWS’s best practices for file storage, EFS is recommended for any workloads requiring NFS support in a cloud environment​​.

Cluster Placement Group: This type of placement group is designed to provide low-latency network performance and high throughput by grouping instances within a single Availability Zone. It is ideal for applications that require tightly coupled node-to-node communication.

Configuration:

When launching EC2 instances, specify the option to launch them in a cluster placement group.

This ensures that the instances are physically located close to each other, reducing latency and increasing network throughput.

Benefits:

Low-Latency Communication: Instances in a cluster placement group benefit from enhanced networking capabilities, enabling low-latency communication.

High Network Throughput: The network performance within a cluster placement group is optimized for high throughput, which is essential for HPC workloads.

VPC Endpoint for S3: A gateway endpoint for Amazon S3 enables you to privately connect your VPC to S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Configuration Steps:

In the VPC console, navigate to "Endpoints" and create a new endpoint.

Select the service name for S3 (com.amazonaws.region.s3).

Choose the VPC and the subnets where your EC2 instances are running.

Update the route tables for the selected subnets to include a route pointing to the endpoint.

Security Compliance: By configuring an S3 gateway endpoint, all traffic between the VPC and S3 stays within the AWS network, complying with the company's security regulations to avoid internet traversal.

To analyze the performance of the web application with granularity of no more than 2 minutes, enablingdetailed monitoringon EC2 instances is the best solution. By default, CloudWatch provides metrics at a 5-minute interval. Enabling detailed monitoring allows you to collect metrics at 1-minute intervals, which will give you the level of granularity you need to analyze performance during peak traffic.

Amazon CloudWatch metrics can then be used to analyze CPU utilization, memory usage, disk I/O, and network throughput, among other performance-related metrics, at the desired granularity.

Option A: Sending CloudWatch logs to Redshift for analysis is unnecessary and overcomplicated for simple performance analysis, which can be done using CloudWatch metrics alone.

Option C: Fetching EC2 logs via Lambda adds complexity, and CloudWatch metrics already provide the required data for performance analysis.

Option D: Sending logs to S3 and using Redshift for analysis is also more complex than necessary for simple performance monitoring.

AWS References:

Monitoring Amazon EC2 with CloudWatch

Amazon CloudWatch Detailed Monitoring

Given the large size (180 TB) of the database and the time constraint,AWS Snowball Edge Storage Optimizedis the best solution. Snowball Edge allows for the physical transfer of large datasets to AWS efficiently without relying on slow internet connections.AWS DMSandSCTcan be used to perform ongoing replication of any changes made during the migration, ensuring minimal downtime.

Option B (VPN): Using a 100 Mbps internet connection would take far too long to transfer 180 TB.

Option C (Direct Connect): Establishing a 10 Gbps Direct Connect link might not be feasible within the 2-week timeframe.

Option D (DataSync over internet): With the existing internet connection, DataSync would also take too long.

AWS References:

AWS Snowball Edge

AWS DMS

Amazon RDSMulti-AZ DB cluster deploymentensures high availability by automatically replicating data across multiple Availability Zones (AZs), and it supports failover in case of a failure in one AZ. This setup also provides increased capacity for read workloads by allowing read scaling with reader instances in different AZs. This solution offers the most operational efficiency with minimal manual intervention.

Option A (DynamoDB): DynamoDB is not suitable for a relational database workload, which requires a PostgreSQL engine.

Option B (RDS with Multi-AZ): While this provides high availability, it doesn't offer read scaling capabilities.

Option D (Cross-Region Read Replicas): This adds complexity and is not necessary if the requirement is high availability within a single region.

AWS References:

Amazon RDS Multi-AZ DB Cluster

A. Amazon EFS:Provides a scalable, shared file storage solution with minimal operational overhead. It's ideal for Linux-based workloads.

B. Amazon FSx for NetApp ONTAP:More suited for workloads requiring NetApp-specific features.

C. Amazon EBS:Requires manual management of file shares, which increases operational overhead.

D. Amazon FSx for Windows File Server:Best suited for Windows SMB workloads with low operational overhead.

E. Amazon FSx for OpenZFS:Better for Linux and Unix-based workloads.

To process each form exactly once and ensure no data is lost, using an Amazon SQS FIFO (First-In-First-Out) queue is the most appropriate solution. SQS FIFO queues guarantee that messages are processed in the exact order they are sent and ensure that each message is processed exactly once. This ensures data consistency and reliability, both of which are crucial for processing user-submitted forms without data loss.

SQS acts as a buffer between the web application server and the worker tier, ensuring that submitted forms are stored reliably and forwarded to the worker tier for processing. This also decouples the application, improving its scalability and resilience.

Option B (API Gateway): API Gateway is better suited for API management rather than acting as a message queue for form processing.

Option C (SQS Standard Queue): While SQS Standard queues offer high throughput, they do not guarantee exactly-once processing or the strict ordering needed for this use case.

Option D (Step Functions): Step Functions are useful for orchestrating workflows but add unnecessary complexity for simple message queuing and form processing.

AWS References:

Amazon SQS FIFO Queues

Decoupling Application Tiers Using Amazon SQS

To securely integrate Amazon S3 with an application that uses Amazon Cognito for user authentication, the following two steps are essential:

Step 1: Create an Amazon Cognito Identity Pool (Option A)

Amazon Cognito Identity Poolsallow users to obtain temporary AWS credentials to access AWS resources, such as Amazon S3, after successfully authenticating with the Cognito user pool. The identity pool bridges the gap between user authentication and AWS service access by generating temporary credentials using AWS Identity and Access Management (IAM).

Once a user logs in using theCognito User Pool, the identity pool providesIAM roles with specific permissionsthat the application can use to access S3 securely. This ensures that each user has appropriate access controls while accessing the S3 bucket.

This is a secure way to ensure that users only have temporary and least-privilege access to the S3 bucket for their documents.

Step 2: Create an Amazon S3 VPC Endpoint (Option C)

By creating anAmazon S3 VPC endpoint, the company ensures that communication between the application (which is hosted in a private subnet) and the S3 bucket occurs over theAWS private network, without the need to traverse the internet. This enhances security and prevents exposure of data to public networks.

TheVPC endpointallows the application to access the S3 bucket privately and securely within the VPC. It also ensures that traffic stays within the AWS network, reducing attack surface and improving overall security.

Why the Other Options Are Incorrect:

Option B: This is incorrect becauseAmazon Cognito User Poolsare used for user authentication, not for generating S3 access tokens. To provide S3 access, you need to useAmazon Cognito Identity Pools, which offer AWS credentials.

Option D: ANAT gatewayis unnecessary in this scenario. Using aVPC endpointfor S3 access provides a more secure and cost-effective solution by keeping traffic within AWS.

Option E: Attaching a policy to restrict access based on IP addresses is not scalable or efficient. It would require managing users’ dynamic IP addresses, which is not an effective security measure for this use case.

AWS References:

Amazon Cognito Identity Pools

Amazon VPC Endpoints for S3

This solution is the most suitable for running cron jobs on AWS with minimal refactoring, while also supporting the possibility of running jobs in response to future events.

Container Image for Cron Jobs: By containerizing the cron jobs, you can package the environment and dependencies required to run the jobs, ensuring consistency and ease of deployment across different environments.

Amazon EventBridge Scheduler: EventBridge Scheduler allows you to create a recurring schedule that can trigger tasks (like running your cron jobs) at specific times or intervals. It provides fine-grained control over scheduling and integrates seamlessly with AWS services.

AWS Fargate: Fargate is a serverless compute engine for containers that removes the need to manage EC2 instances. It allows you to run containers without worrying about the underlying infrastructure. Fargate is ideal for running jobs that can vary in duration, like cron jobs, as it scales automatically based on the task's requirements.

Why Not Other Options?:

Option A (Lambda): While AWS Lambda could handle short-running cron jobs, it has limitations in terms of execution duration (maximum of 15 minutes) and might not be suitable for jobs that run up to 20 minutes.

Option B (AWS Batch on ECS): AWS Batch is more suitable for batch processing and workloads that require complex job dependencies or orchestration, which might be more than what is needed for simple cron jobs.

Option D (Step Functions with Wait State): While Step Functions provide orchestration capabilities, this approach would introduce unnecessary complexity and overhead compared to the straightforward scheduling with EventBridge and running on Fargate.

AWS References:

Amazon EventBridge Scheduler- Details on how to schedule tasks using Amazon EventBridge Scheduler.

AWS Fargate- Information on how to run containers in a serverless manner using AWS Fargate.

Amazon Aurora Serverless is a cost-effective, on-demand, autoscaling configuration for Amazon Aurora. It automatically adjusts the database's capacity based on the current demand, which is ideal for workloads with variable and unpredictable usage patterns. Since the application is expected to be read-heavy with occasional writes and steady growth, Aurora Serverless can provide the necessary performance without requiring the management of database instances.

Cost-Optimization: Aurora Serverless only charges for the database capacity you use, making it a more cost-effective solution compared to always running provisioned database instances, especially for workloads with fluctuating demand.

Scalability: It automatically scales database capacity up or down based on actual usage, ensuring that you always have the right amount of resources available.

Performance: Aurora Serverless is built on the same underlying storage as Amazon Aurora, providing high performance and availability.

Why Not Other Options?:

Option A (RDS with Provisioned IOPS SSD): While Provisioned IOPS SSD ensures consistent performance, it is generally more expensive and less flexible compared to the autoscaling nature of Aurora Serverless.

Option C (DynamoDB with On-Demand Capacity): DynamoDB is a NoSQL database and may not be the best fit for applications requiring relational database features.

Option D (RDS with Magnetic Storage and Read Replicas): Magnetic storage is outdated and generally slower. While read replicas help with read-heavy workloads, the overall performance might not be optimal, and magnetic storage doesn’t provide the necessary performance.

AWS References:

Amazon Aurora Serverless- Information on how Aurora Serverless works and its use cases.

Amazon Aurora Pricing- Details on the cost-effectiveness of Aurora Serverless.

Option B: FSx for Lustre is designed for HPC workloads with high IOPS.

Option E: A cluster placement group ensures low-latency networking for HPC analytics workloads.

Option A: Amazon EFS is not optimized for HPC.

Option D: Mountpoint for S3 does not meet high IOPS needs.

The most cost-effective and scalable solution for generating thumbnails when photos are uploaded to an S3 bucket is to useS3 event notificationsto trigger anAWS Lambda function. This approach avoids the need for a long-running EC2 instance or EMR cluster, making it highly cost-effective because Lambda only charges for the time it takes to process each event.

S3 Event Notifications: Automatically triggers the Lambda function when a new photo is uploaded to the S3 bucket.

AWS Lambda: A serverless compute service that scales automatically and only charges for execution time, which makes it the most economical choice when dealing with periodic events like photo uploads.

The Lambda function can generate the thumbnail and upload it to a second S3 bucket, fulfilling the requirement efficiently.

Option AandOption B(EMR or EC2 with scheduled scripts)**: These are less cost-effective as they involve continuously running infrastructure, which incurs unnecessary costs.

Option D (S3 Storage Lens): S3 Storage Lens is a tool for storage analytics and is not designed for event-based photo processing.

AWS References:

Amazon S3 Event Notifications

AWS Lambda Pricing

Creating a listener rule on theApplication Load Balancer (ALB)to return a maintenance response during the maintenance window is the most straightforward solution with the least operational overhead. The rule can be configured to match all incoming requests and return a custom response, and it can be easily removed once maintenance is complete.

Option A (Aurora table flag): This adds unnecessary complexity for a temporary maintenance response.

Option B and D (SQS or SNS): These options introduce more components than needed for a simple maintenance message.

AWS References:

ALB Listener Rules

AWS Direct Connect: Provides a dedicated network connection from your on-premises data center to AWS, ensuring low latency and consistent network performance.

Direct Connect Gateway Association:

Direct Connect Gateway: Acts as a global network transit hub to connect VPCs across different AWS regions.

Association with Transit Gateway: Enables communication between on-premises data centers and multiple VPCs connected to the transit gateway.

Transit Virtual Interface (VIF):

Create Transit VIF: To connect Direct Connect with a transit gateway.

Setup Steps:

Establish a Direct Connect connection.

Create a transit VIF to the Direct Connect gateway.

Associate the Direct Connect gateway with the transit gateway attached to the VPCs.

Cost Efficiency: This combination avoids the recurring costs and potential performance variability of VPN connections, providing a robust, low-latency hybrid network solution.

BothAmazon KinesisandSQS FIFOqueues ensure the sequential processing of messages. By using the payment ID as the partition key in Kinesis or as the message group in the SQS FIFOqueue, messages are processed in order. Both solutions also allow for long-term retention (up to 10 days) of messages, making them suitable for this payment processing use case.

Option A (DynamoDB): DynamoDB does not guarantee message ordering for real-time processing.

Option C (ElastiCache): ElastiCache is for caching, not suitable for sequential message processing.

Option D (Standard SQS queue): A standard SQS queue does not guarantee ordering of messages.

AWS References:

Amazon Kinesis

Amazon SQS FIFO Queues

S3 One Zone-IA:

Suitable for infrequently accessed data that doesn't require multiple Availability Zone resilience.

Cost-effective for storing user-uploaded images that are only used for AI model training twice a year.

S3 Standard:

Ideal for frequently accessed data with high durability and availability.

Store premium user-generated AI images here to ensure they are readily available for download at any time.

S3 Standard-IA:

Cost-effective storage for data that is accessed less frequently but still requires rapid retrieval.

Store non-premium user-generated AI images here, as these images are only downloaded once every 6 hours, making it a good balance between cost and accessibility.

Cost-Effectiveness: This solution optimizes storage costs by categorizing data based on access patterns and durability requirements, ensuring that each type of data is stored in the most cost-effective manner.

Amazon S3 Standard-IA: This storage class is designed for data that is accessed less frequently but requires rapid access when needed. It offers lower storage costs compared to S3 Standard while still providing high availability and durability.

Access Patterns: Since the files are frequently accessed in the first 30 days and rarely accessed afterward, transitioning them to S3 Standard-IA after 30 days aligns with their access patterns and reduces storage costs significantly.

Lifecycle Policy: Implementing a lifecycle policy to transition the files to S3 Standard-IA ensures automatic management of the data lifecycle, moving files to a lower-cost storage class without manual intervention. Deleting the files after 4 years further optimizes costs by removing data that is no longer needed.

Amazon CloudFront is a content delivery network (CDN) service that caches copies of your content at edge locations around the world. This helps improve performance by serving content from the edge nearest to the user. However, when the content in Amazon S3 (your origin) is updated, those updates may not immediately reflect on the website if they are cached at the CloudFront edge locations.

The issue described in the question suggests that the CI/CD pipeline is functioning correctly, and updates are being deployed to S3. However, since CloudFront caches this content, the edge locations may still be serving outdated content, causing the updates to not be reflected on the website.

To resolve this issue, you need to invalidate the CloudFront cache. By invalidating the cache, CloudFront will remove the outdated content and retrieve the latest version from the S3 origin.

AWS documentation on this process:

CloudFront cache invalidation allows you to clear items from the cache so that CloudFront retrieves the latest version from the origin. You can create invalidation requests via the AWS Management Console, AWS CLI, or SDKs.

AWS CloudFront Documentation

Why the other options are incorrect:

A. Add an Application Load Balancer: ALBs are used to distribute incoming application traffic and are not relevant to caching or serving content from CloudFront.

B. Add Amazon ElastiCache for Redis or Memcached: This would help in caching database queries but has no relation to static website content hosted on CloudFront and S3.

D. Use AWS Certificate Manager (ACM): ACM is used for managing SSL/TLS certificates and is unrelated to the issue of content not being updated on CloudFront.

Amazon Route 53 Multivalue Answer Routing: This routing policy allows Route 53 to return multiple values, such as IP addresses, in response to DNS queries. This can distribute traffic across multiple resources and includes health checks to ensure traffic is only routed to healthy instances.

Health Checks:

Configure health checks for each Region to monitor the health of the website instances.

Route 53 will only include healthy instances in the DNS responses, ensuring that traffic is not routed to an unhealthy Region.

Load Distribution and Disaster Recovery:

Multivalue answer routing helps balance the load between instances in different Regions.

If instances in one Region become unhealthy, Route 53 will route traffic to the healthy instances in the other Region.

Operational Simplicity: This solution does not require complex configurations or additional resources, making it a simple yet effective way to distribute traffic and ensure high availability.

AWS Organizations: AWS Organizations allows you to create multiple AWS accounts and manage them centrally. You can organize accounts into organizational units (OUs) and apply policies to these units.

Organizational Units (OUs):

Create separate OUs for each department: finance, data analytics, and development.

Place the respective AWS accounts for each department into their corresponding OUs.

Service Control Policies (SCPs):

SCPs are policies that can restrict which AWS services and actions are available to accounts in an OU.

Create SCPs to define which services each department can use and attach these policies to the appropriate OUs.

SCPs apply to all IAM users, groups, and roles within the accounts in the OU, providing centralized control over service usage.

Operational Efficiency: Using AWS Organizations and SCPs provides a scalable and centralized way to manage permissions across multiple accounts with minimal operational overhead.

Setting the RDS backup retention policy to 30 days forautomated backupsthrough AWS Backup allows the company to retain backups cost-effectively. Manual backups, however, are notautomatically managed by RDS's retention policy, so they need to be manually deleted if they are older than 30 days to avoid unnecessary storage costs.

Key AWS features:

Automated Backups: Can be configured with a retention policy of up to 35 days, ensuring that older automated backups are deleted automatically.

Manual Backups: These are not subject to the automated retention policy and must be manually managed to avoid extra costs.

AWS Documentation: AWS recommends using backup retention policies for automated backups while manually managing manual backups​.

Memory Optimized Instances: These instances are designed to deliver fast performance for workloads that process large data sets in memory. They are ideal for high-performance databases like SAP and applications with high memory utilization.

High Memory Utilization: Both the SAP application and the SQL Server database have high memory demands as per the on-premises performance data. Memory optimized instances provide the necessary memory capacity and performance.

Instance Types:

For the SAP application, using a memory optimized instance ensures the application has sufficient memory to handle the high workload efficiently.

For the SQL Server database, memory optimized instances ensure optimal database performance with high memory throughput.

Operational Efficiency: Using the same instance family for both the application and the database simplifies management and ensures both components meet performance requirements.

This solution meets the requirements of providing encryption at both the network and session layers while also allowing for controlled access between on-premises systems and AWS resources.

AWS Site-to-Site VPN: This service allows you to establish a secure and encrypted connection between your on-premises network and AWS VPC over the internet or via AWS Direct Connect. The VPN encrypts data at the network layer (IPsec) as it travels between the corporate network and AWS.

Routing and Security Controls: By configuring route table entries, you can ensure that only the traffic intended for AWS resources is directed to the VPC. Additionally, by setting up security groups and network ACLs, you can further restrict and control which traffic is allowed to communicate with the instances within your VPC. This approach provides the necessary security to prevent unrestricted access, aligning with the company’s security policies.

Why Not Other Options?:

Option A (AWS Direct Connect): While Direct Connect provides a private connection, it does not inherently provide encryption. Additional steps would be required to encrypt traffic, and it doesn’t address the session layer encryption.

Option B (IAM policies for Console access): This option does not meet the requirement for network-level encryption and security between the corporate network and the VPC.

Option D (AWS Transit Gateway): Although Transit Gateway can help in managing multiple connections, it doesn’t directly provide encryption at the network layer. You would still need to configure a VPN or use other methods for encryption.

AWS References:

AWS Site-to-Site VPN- Overview of AWS Site-to-Site VPN capabilities, including encryption.

Security Groups and Network ACLs- Information on configuring security groups and network ACLs to control traffic.

Amazon FSx for Lustre is the ideal solution for high-performance computing (HPC) workloads that require parallel access to a shared file system with low latency. FSx for Lustre is designed specifically to meet the needs of such workloads, offering sub-millisecond latencies, which makes it well-suited for the 1 ms latency requirement mentioned in the question.

Here is why FSx for Lustre is the best fit:

Parallel File System: FSx for Lustre is a parallel file system that can scale across hundreds of Amazon EC2 instances, providing high throughput and low-latency access to data. It is optimized for processing large datasets in parallel, which is essential for HPC workloads.

Low Latency: FSx for Lustre is capable of providing access latencies well within 1 ms, making it ideal for performance-sensitive workloads like HPC.

Seamless Integration with Amazon S3: FSx for Lustre can be linked to an Amazon S3 bucket. This integration allows data to be imported from S3 into FSx for Lustre before the workload begins and exported back to S3 after processing. This feature is crucial for manual postprocessing because it enables engineers to access the dataset in S3 after processing.

Performance: FSx for Lustre is built for workloads that require high performance, such as machine learning, analytics, media processing, and financial simulations, which are typical for HPC environments.

In contrast:

Amazon EFS (Option A): While EFS provides shared file storage and scales across multiple EC2 instances, it does not offer the same level of performance or sub-millisecond latencies as FSx for Lustre. EFS is more suited for general-purpose workloads, not high-performance computing.

Mounting S3 as a file system (Option B and D): S3 is object storage, not a file system designed for low-latency access and parallel processing. Mounting S3 buckets directly or using AWS Resource Access Manager to share the bucket would not meet the low-latency (1 ms) or performance requirements needed for HPC workloads.

Therefore, Amazon FSx for Lustre (Option C) is the most appropriate and verified solution for this scenario.

AWS References:

Amazon FSx for Lustre

Best Practices for High Performance Computing (HPC)

Amazon FSx and Amazon S3 Integration

AWSSecrets Manageris the best solution for securely storing and managing sensitive information, such as usernames and passwords. Secrets Manager provides automatic rotation, fine-grained access control, and encryption of credentials. It is designed to integrate easily with other AWS services, such as CloudFormation, to automate the retrieval of secrets via theBatchGetSecretValue API.

Secrets Manager has a lower operational overhead than manually managing credentials, and it offers features like automatic secret rotation that reduce the need for human intervention.

Option A and C (Parameter Store): While Systems Manager Parameter Store can store secrets, Secrets Manager provides more specialized capabilities for securely managing and rotating credentials with less operational overhead.

Option B and C (AWS Batch): Introducing AWS Batch unnecessarily complicates the solution. Secrets Manager already provides simple API calls for retrieving secrets without needing an additional service.

AWS References:

AWS Secrets Manager

Secrets Manager with CloudFormation

Edge-optimized API endpointsroute requests through CloudFront, reducing latency for global users.

Option Acorrectly implements edge-optimization, caching, and compression to minimize latency.

Options B and Ddo not use edge optimization, leading to higher latency for global users.

Reserved concurrency inOptions C and Dimproves backend scaling but does not address global latency directly.

AWS Fargate is a serverless experience for user applications, allowing the user to concentrate on building applications instead of configuring and managing servers. Fargate also automates resource management, allowing users to easily scale their applications in response to demand.

EC2 Spot Instances allow users to bid on spare Amazon EC2 computing capacity and can be a cost-effective solution for stateless, interruptible workloads that can be started and stopped at any time. Since the batch processing job is stateless, can be started and stopped at any time, and typically takes upwards of 60 minutes to complete, EC2 Spot Instances would be a good fit for this workload.

Many applications, including those built on modern serverless architectures, can have a large number of open connections to the database server and may open and close database connections at a high rate, exhausting database memory and compute resources. Amazon RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability.https://aws.amazon.com/id/rds/proxy/

Before you begin: Decide which two Availability Zones you will use for your EC2 instances. Configure your virtual private cloud (VPC) with at least one public subnet in each of these Availability Zones. These public subnets are used to configure the load balancer. You can launch your EC2 instances in other subnets of these Availability Zones instead.

Use a group email address for the management account's root userhttps://docs.aws.amazon.com/organizations/latest/userguide/orgs_best-practices_mgmt-acct.html#best-practices_mgmt-acct_email-address

Amazon CloudFront is a content delivery network (CDN) that speeds up the delivery of static and dynamic web content, such as HTML pages, images, and videos. By using CloudFront, the HTML pages will be served to users from the edge location that is closest to them, resulting in faster delivery and a better user experience. CloudFront can also handle the high traffic and large number of requests expected for the global event, ensuring that the HTML pages are available and accessible to users around the world.

ElastiCache can help speed up the read performance of the database by caching frequently accessed data, reducing latency and allowing the application to access the data more quickly. This solution requires minimal modifications to the current architecture, as ElastiCache can be used in conjunction with the existing Amazon RDS for MySQL database.

Composite alarms determine their states by monitoring the states of other alarms. You can **use composite alarms to reduce alarm noise**. For example, you can create a composite alarm where the underlying metric alarms go into ALARM when they meet specific conditions. You then canset up your composite alarm to go into ALARM and send you notifications when the underlying metric alarms go into ALARM by configuring the underlying metric alarms never to take actions. Currently, composite alarms can take the following actions:https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Create_Composite_Alarm.html

Service control policies (SCPs) are one type of policy that you can use to manage your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing you to ensure your accounts stay within your organization's access control guidelines. Seehttps://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scp.html.

https://aws.amazon.com/about-aws/whats-new/2017/10/aws-waf-now-supports-geographic-match/

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ecs-taskdefinition.html

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_RestoreFromSnapshot.html#USER_RestoreFromSnapshot.CON

Under "Encrypt unencrypted resources" -https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSEncryption.html

https://aws.amazon.com/kms/faqs/#:~:text=If%20you%20are%20a%20developer%20who%20needs%20to%20digitally,a%20broad%20set%20of%20industry%20and%20regional%20compliance%20regimes.

This solution meets the requirements of a customer-facing application that has a clearly defined access pattern throughout the year and a variable number of reads and writes that depend on the time of year. Amazon DynamoDB is a fully managed NoSQL database service that can handle any level of request traffic and data size. DynamoDB auto scaling can automatically adjust the provisioned read and write capacity based on the actual workload. DynamoDB on-demand backups can create full backups of the tables for data protection and archival purposes. DynamoDB Streams can capture a time-ordered sequence of item-level modifications in the tables for audit purposes.

Option B is incorrect because Amazon Redshift is a data warehouse service that is designed for analytical workloads, not for customer-facing applications. Option C is incorrect because Amazon RDS with Provisioned IOPS can provide consistent performance for relational databases, but it may not be able to handle unpredictable spikes in traffic and data size. Option D is incorrect because Amazon Aurora MySQL with auto scaling can provide high performance and availability for relational databases, but it does not support audit logging as a parameter.

1. database shows no signs of being overloaded. CPU, memory, and disk access metrics are all low==>A and C out. We cannot only add nodes instance or add read replica, because database workload is totally fine, very low. 2. "least operational overhead"==>B out, because b need to configure lambda. 3. ROS proxy: Shares infrequently used connections; High availability with failover; Drives increased efficiency==>proxy can leverage failover to redirect traffic from timeout rds instance to healthy rds instance. So D is right.

https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps_examples_vpc.html#example_vpc_2

AWS Database Migration Service supports homogeneous migrations such as Oracle to Oracle, as well as heterogeneous migrations between different database platforms, such as Oracle or Microsoft SQL Server to Amazon Aurora. With AWS Database Migration Service, you can also continuously replicate data with low latency from any supported source to any supported target. For example, you can replicate from multiple sources to Amazon Simple Storage Service (Amazon S3) to build a highly available and scalable data lake solution. You can also consolidate databases into a petabyte-scale data warehouse by streaming data to Amazon Redshift. Learn more about the supported source and target databases.https://aws.amazon.com/dms/

This solution meets the requirements of a disaster recovery solution to back up the data that is generated by an analytics application, stored in JSON format, and must be accessible in milliseconds if it is needed. Amazon S3 Standard is a durable and scalable storage class for frequently accessed data. It can store any amount of data and provide high availability and performance. It can also support millisecond access time for data retrieval.

Option A is incorrect because Amazon OpenSearch Service (Amazon Elasticsearch Service) is a search and analytics service that can index and query data, but it is not a backup solution for data stored in JSON format. Option B is incorrect because Amazon S3 Glacier is a low-cost storage class for data archiving and long-term backup, but it does not support millisecond access time fordata retrieval. Option D is incorrect because Amazon RDS for PostgreSQL is a relational database service that can store and query structured data, but it is not a backup solution for data stored in JSON format.

For standard accelerators, Global Accelerator uses the AWS global network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure, which increases the availability of your applications. Endpoints for standard accelerators can be Network Load Balancers, Application Load Balancers, Amazon EC2 instances, or Elastic IP addresses that are located in one AWS Region or multiple Regions.

https://docs.aws.amazon.com/global-accelerator/latest/dg/what-is-global-accelerator.html

This solution meets the requirements of a two-tier application that has a variable demand based on the time of day and must be available at all times, while minimizing the overall cost. EC2 Reserved Instances can provide significant savings compared to On-Demand Instances for the baseline level of usage, and they can guarantee capacity reservation when needed. EC2 Spot Instances can provide up to 90% savings compared to On-Demand Instances for any additional capacity that the application needs during peak hours. Spot Instances are suitable for stateless applications that can tolerate interruptions and can be replaced by other instances. Stopping the RDS database when it is not in use can reduce the cost of running the database tier.

Option A is incorrect because using all EC2 Spot Instances can affect the availability of the application if there are not enough spare capacity or if the Spot price exceeds the maximum price. Stopping the RDS database when it is not in use can reduce the cost of running the database tier, but it can also affect the availability of the application. Option B is incorrect because purchasing EC2 Instance Savings Plans to cover five EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Purchasing an RDS Reserved DB Instance can provide savings for the database tier, but it does not allow stopping the database when it is not in use. Option D is incorrect becausepurchasing EC2 Instance Savings Plans to cover two EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Using up to three additional EC2 On-Demand Instances as needed can incur higher costs than using Spot Instances.

https://aws.amazon.com/certificate-manager/:

"With AWS Certificate Manager, you can quickly request a certificate, deploy it on ACM-integrated AWS resources, such as Elastic Load Balancers, Amazon CloudFront distributions, and APIs on API Gateway, and let AWS Certificate Manager handle certificate renewals. It also enables you to create private certificates for your internal resources and manage the certificate lifecycle centrally."

https://aws.amazon.com/cn/blogs/compute/cost-optimization-and-resilience-eks-with-spot-instances/

High availability can be enabled for this architecture quite simply by modifying the existing Auto Scaling group to use multiple availability zones. The ASG will automatically balance the load so you don't actually need to specify the instances per AZ.

This solution meets the requirements of creating digital copies for a large collection of historical written records, analyzing the documents, extracting the medical information, and storing the documents so that an application can run SQL queries on the data. Writing the document information to an Amazon S3 bucket can provide scalable and durable storage for the scanned files. Using Amazon Athena to query the data can provide serverless and interactive SQL analysis on data stored in S3. Creating an AWS Lambda function that runs when new documents are uploaded can provide event-driven and serverless processing of the scanned files. Using Amazon Textract to convert the documents to raw text can provide accurate optical character recognition (OCR) and extraction of structured data such as tables and forms from documents using artificial intelligence (AI). Using Amazon Comprehend Medical to detect and extract relevant medical information from the text can provide natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text.

Option A is incorrect because writing the document information to an Amazon EC2 instance that runs a MySQL database can increase the infrastructure overhead and complexity, and it may not be able to handle large volumes of data. Option C is incorrect because creating an Auto Scaling group of Amazon EC2 instances to run a custom application that processes the scanned files and extracts the medical information can increase the infrastructure overhead and complexity, and it may not be able to leverageexisting AI and NLP services such as Textract and Comprehend Medical. Option D is incorrect because using Amazon Rekognition to convert the documents to raw text can provide image and video analysis, but it does not support OCR or extraction of structured data from documents. Using Amazon Transcribe Medical to detect and extract relevant medical information from the text can provide speech-to-text transcription service for medical conversations, but it does not support text analysis or extraction of health data from medical text.

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-restricting-access-to-s3.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/distribution-web-awswaf.html

You need S3 to be able to archive the logs after one month. Cannot do that with CloudWatch Logs.

https://aws.amazon.com/premiumsupport/knowledge-center/cloudwatch-memory-metrics-ec2/

https://docs.aws.amazon.com/sns/latest/dg/sns-dead-letter-queues.html

https://aws.amazon.com/blogs/compute/microservice-delivery-with-amazon-ecs-and-application-load-balancers/

This solution meets the requirements of running a gaming application that transmits data by using UDP packets and scaling out and in as traffic increases and decreases. A Network Load Balancer can handle millions of requests per second while maintaining high throughput at ultra low latency, and it supports both TCP and UDP protocols. An Auto Scaling group can automatically adjust the number of EC2 instances based on the demand and the scaling policies.

Option B is incorrect because an Application Load Balancer does not support UDP protocol, only HTTP and HTTPS. Option C is incorrect because Amazon Route 53 is a DNS service that can route traffic based on different policies, but it does not provide load balancing or scaling capabilities. Option D is incorrect because a NAT instance is used to enable instances in a private subnet to connect to the internet or other AWS services, but it does not provide load balancing or scaling capabilities.

https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/monitor-ami-events.html#:~:text=For%20example%2C%20you%20can%20create%20an%20EventBridge%20rule%20that%20detects%20when%20the%20AMI%20creation%20process%20has %20completed%20and%20then%20invokes%20an%20Amazon%20SNS%20topic%20to%20send%20an%20email%20notification%20to%20you.

Creating an Amazon EventBridge (Amazon CloudWatch Events) rule for the CreateImage API call and configuring the target as an Amazon Simple Notification Service (Amazon SNS) topic to send an alert when a CreateImage API call is detected will meet the requirements with the least operational overhead. Amazon EventBridge is a serverless event bus that makes it easy to connect applications together using data from your own applications, integrated Software as a Service (SaaS) applications, and AWS services. By creating an EventBridge rule for the CreateImage API call, the company can set up alerts whenever this operation is called within their account. The alert can be sent to an SNS topic, which can then be configured to send notifications to the company's email or other desired destination.

In a typical development environment, dev and test databases are mostly utilized for 8 hours a day and sit idle when not in use. However, the databases are billed for the compute and storage costs during this idle time. To reduce the overall cost, Amazon RDS allows instances to be stopped temporarily. While the instance is stopped, you’re charged for storage and backups, but not for the DB instance hours. Please note that a stopped instance will automatically be started after 7 days. This post presents a solution using AWS Lambda and Amazon EventBridge that allows you to schedule a Lambda function to stop and start the idle databases with specific tags to save on compute costs. The second post presents a solution that accomplishes stop and start of the idle Amazon RDS databases using AWS Systems Manager.

https://docs.aws.amazon.com/whitepapers/latest/aws-vpc-connectivity-options/aws-direct-connect-aws-transit-gateway.html

To make all the data available to various teams and minimize operational overhead, the company can create a data lake by using AWS Lake Formation. This will allow the company to centralize all the data in one place and use fine-grained access controls to manage access to the data. To meet the requirements of the company, the solutions architect can create a data lake by using AWS Lake Formation, create an AWS Glue JDBC connection to Amazon RDS, and register the S3 bucket in Lake Formation. The solutions architect can then use Lake Formation access controls to limit access to the data. This solution will provide the ability to manage fine-grained permissions for the data and minimize operational overhead.

https://aws.amazon.com/about-aws/whats-new/2018/04/announcing-s3-one-zone-infrequent-access-a-new-amazon-s3-storage-class/?nc1=h_ls

This approach ensures that the order creation and payment processing steps are separate and atomic. By sending the order information to an SQS FIFO queue, the payment service can process the order one at a time and in the order they were received. If the payment service is unable to process an order, it can be retried later, preventing the creation of multiple orders. The deletion of the message from the queue after it is processed will prevent the same message from being processed multiple times.

CloudFront uses a local cache to provide the response, AWS Global accelerator proxies requests and connects to the application all the time for the response.

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-restricting-access-to-s3.html#private-content-granting-permissions-to-oai

Amazon Athena is the best choice for running one-time queries on streaming data. Although Amazon Kinesis Data Analytics provides an easy and familiar standard SQL language to analyze streaming data in real-time, it is designed for continuous queries rather than one-time queries[1]. On the other hand, Amazon Athena is a serverless interactive query service that allows querying data in Amazon S3 using SQL. It is optimized for ad-hoc querying and is ideal for running one-time queries on streaming data[2].AWS Lake Formation uses as a central place to have all your data for analytics purposes (E). Athena integrate perfect with S3 and can makes queries (A).

https://aws.amazon.com/dynamodb/dax/

SSE-S3 - is free and uses AWS owned CMKs (CMK = Customer Master Key). The encryption key is owned and managed by AWS, and is shared among many accounts. Its rotation is automatic with time that varies as shown in the table here. The time is not explicitly defined.

SSE-KMS - has two flavors:

AWS managed CMK. This is free CMK generated only for your account. You can only view it policies and audit usage, but not manage it. Rotation is automatic - once per 1095 days (3 years),

Customer managed CMK. This uses your own key that you create and can manage. Rotation is not enabled by default. But if you enable it, it will be automatically rotated every 1 year. This variant can also use an imported key material by you. If you create such key with an imported material, there is no automated rotation. Only manual rotation.

SSE-C - customer provided key. The encryption key is fully managed by you outside of AWS. AWS will not rotate it.

This solution meets the requirements of moving data to an Amazon S3 bucket, encrypting the data when it is stored in the S3 bucket, and automatically rotating the encryption key every year with the least operational overhead. AWS Key Management Service (AWS KMS) is a service that enables you to create and manage encryption keys for your data. A customer managed key is a symmetric encryption key that you create and manage in AWS KMS. You can enable automatic key rotation for a customer managed key, which means that AWS KMS generates new cryptographic material for the key every year. You can set the S3 bucket’s default encryption behavior to use the customer managed KMS key, which means that any object that is uploaded to the bucket without specifying an encryption method will be encrypted with that key.

Option A is incorrect because using server-side encryption with Amazon S3 managed encryption keys (SSE-S3) does not allow you to control or manage the encryption keys. SSE-S3 uses a unique key for each object, and encrypts that key with a master key that is regularly rotated by S3. However, you cannot enable or disable key rotation for SSE-S3 keys, or specify the rotation interval. Option C is incorrect because manually rotating the KMS key every year can increase the operational overhead and complexity, and it may not meet the requirement of rotating the key every year if you forget or delay the rotationprocess. Option D is incorrect because encrypting the data with customer key material before moving the data to the S3 bucket can increase the operational overhead and complexity, and it may not provide consistent encryption for all objects in the bucket. Creating a KMS key without key material and importing the customer key material into the KMS key can enable you to use your own source of random bits to generate your KMS keys, but it does not support automatic key rotation.

Amazon Aurora is a fully managed, scalable, and highly available relational database service that is compatible with PostgreSQL. Migrating the database to Amazon Aurora would reduce the operational overhead of maintaining the database infrastructure and allow the company to focus on building and scaling the application. AWS Fargate is a fully managed container orchestrationservice that enables users to run containers without the need to manage the underlying EC2 instances. By using AWS Fargate with Amazon Elastic Container Service(Amazon ECS), the solutions architect can improve the scalability and efficiency of the web application and reduce the operational overhead of maintaining the underlying infrastructure.

AWS Global Accelerator and Amazon CloudFront are separate services that use the AWS global network and its edge locations around the world. CloudFront improves performance for both cacheable content (such as images and videos) and dynamic content (such as API acceleration and dynamic site delivery). Global Accelerator improves performance for a wide range of applications over TCP or UDP by proxying packets at the edge to applications running in one or more AWS Regions. Global Accelerator is a good fit for non-HTTP use cases, such as gaming (UDP), IoT (MQTT), or Voice over IP, as well as for HTTP use cases that specifically require static IP addresses or deterministic, fast regional failover. Both services integrate with AWS Shield for DDoS protection.

You can use CloudFront to deliver video on demand (VOD) or live streaming video using any HTTP origin. One way you can set up video workflows in the cloud is by using CloudFront together with AWS Media Services.https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/on-demand-streaming-video.html

Cloudfront for rapid response and s3 to minimize infrastructure.

Reserved is cheaper than on demand the company has. And it's meet the availabilty (HA) requirement as to spot instance that can be disrupted at any time. PRICING BELOW. On-Demand: 0% There’s no commitment from you. You pay the most with this option. Reserved : 40%-60%1-year or 3-year commitment from you. You save money from that commitment. Spot 50%-90% Ridiculously inexpensive because there’s no commitment from the AWS side.

Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, SAP, Zendesk, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks.https://aws.amazon.com/appflow/

https://aws.amazon.com/ebs/features/

"Provisioned IOPS volumes are backed by solid-state drives (SSDs) and are the highest performance EBS volumes designed for your critical, I/O intensive database applications.

These volumes are ideal for both IOPS-intensive and throughput-intensive workloads that require extremely low latency."

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_Storage.html

The correct answer is Option D. Deploy an S3 VPC gateway endpoint into the VPC and attach an endpoint policy that allows access to the S3 buckets. By deploying an S3 VPC gateway endpoint, the application can access the S3 buckets over a private network connection within the VPC, eliminating the need for data transfer over the internet. This can help reduce data transfer fees as well as improve the performance of the application. The endpoint policy can be used to specify which S3 buckets the application has access to.

Creating an Amazon Simple Queue Service (SQS) queue and configuring the S3 bucket to send a notification to the SQS queue when an image is uploaded to the S3 bucket will ensure that the Lambda function is triggered in a stateless and durable manner.

Configuring the Lambda function to use the SQS queue as the invocation source, and deleting the message in the queue after it is successfully processed will ensure that the Lambda function processes the image in a stateless and durable manner.

Amazon SQS is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating-message oriented middleware, and empowers developers to focus on differentiating work. When new images are uploaded to the S3 bucket, SQS will trigger the Lambda function to process the image and compress it. Once the image is processed, the SQS message is deleted, ensuring that the Lambda function is stateless and durable.

To reduce the cost of running the tests without reducing the compute and memory attributes of the Amazon RDS for MySQL DB instance, the development team can stop the instance when tests are completed and restart it when required. Stopping the DB instance when not in use can help save costs because customers are only charged for storage while the DB instance is stopped. During this time, automated backups and automated DB instance maintenance are suspended.When the instance is restarted, it retains the same configurations, security groups, and DB parameter groups as when it was stopped.

https://aws.amazon.com/global-accelerator/faqs/

HTTP /HTTPS - ALB ; TCP and UDP - NLB; Lowest latency routing and more throughput. Also supports failover, uses Anycast Ip addressing - Global Accelerator Caching at Egde Locations – Cloutfront

WS Global Accelerator automatically checks the health of your applications and routes user traffic only to healthy application endpoints. If the health status changes or you make configuration updates, AWS Global Accelerator reacts instantaneously to route your users to the next available endpoint..

https://www.learnaws.org/2020/12/13/aws-rds-proxy-deep-dive/

RDS proxy can improve application availability in such a situation by waiting for the new database instance to be functional and maintaining any requests received from the application during this time. The end result is that the application is more resilient to issues with the underlying database.

This will enable solution to hold data till the time DB comes back to normal. RDS proxy is to optimally utilize the connection between Lambda and DB. Lambda can open multipleconnection concurrently which can be taxing on DB compute resources, hence RDS proxy was introduced to manage and leverage these connections efficiently.

AWS Cost Explorer is a tool that enables you to view and analyze your costs and usage. You can explore your usage and costs using the main graph, the Cost Explorer cost and usage reports, or the Cost Explorer RI reports. You can view data for up to the last 12 months, forecast how muchyou're likely to spend for the next 12 months, and get recommendations for what Reserved Instances to purchase. You can use Cost Explorer to identify areas that need further inquiry and see trends that you can use to understand your costs.https://docs.aws.amazon.com/cost-management/latest/userguide/ce-what-is.html

AWS DataSync is an online data movement and discovery service that simplifies data migration and helps users quickly, easily, and securely move their file or object data to, from, and between AWS storage services1. Users can use AWS DataSync to transfer data between on-premises andAWS storage services. To use AWS DataSync, users need to install an AWS DataSync agent in the on-premises data center. The agent is a software appliance that connects to the source or destination storage system and handles the data transfer to or from AWS over the network2. Users also need to use AWS DataSync to create a suitable location configuration for the on-premises SFTP server. A location is a logical representation of a storage system that contains files or objects that users want to transfer using DataSync. Users can create locations for NFS shares, SMB shares, HDFS file systems, self-managed object storage, Amazon S3 buckets, Amazon EFS file systems, Amazon FSx for Windows File Server file systems, Amazon FSx for Lustre file systems, Amazon FSx for OpenZFS file systems, Amazon FSx for NetApp ONTAP file systems, and AWS Snowcone devices3.

To ensure all Amazon EC2 instances, Amazon RDS DB instances, and Amazon Redshift clusters are configured with tags, a solutions architect should use AWS Config rules to define and detect resources that are not properly tagged. AWS Config rules are a set of customizable rules that AWS Config uses to evaluate AWS resource configurations for compliance with best practices and company policies. Using AWS Config rules can minimize the effort of configuring and operating this check because it automates the process of identifying non-compliant resources and notifying the responsible teams.

To clone the production data into the test environment with high I/O performance and without affecting the production environment, the best option is to take EBS snapshots of the production EBS volumes and restore them onto new EBS volumes in the testenvironment. Then, attach the new EBS volumes to EC2 instances in the test environment. This option minimizes the time required to clone the data and ensures that modifications to the cloned data do not affect the production environment. Therefore, option C is the correct answer.

using AWS ECS on AWS Fargate since they requirements are for scalability and availability without having to provision and manage the underlying infrastructure to run the containerized workload.https://docs.aws.amazon.com/AmazonECS/latest/userguide/what-is-fargate.html

To meet the requirements of immediately accessible records for 1 year and then archived for an additional 9 years with maximum resiliency, we can use S3 Lifecycle policy to transition records from S3 Standard to S3 Glacier Deep Archive after 1 year. And to ensure that the records cannot be deleted by anyone, including administrative and root users, we can use S3 Object Lock in compliance mode for a period of 10 years. Therefore, the correct answer is option C.

To provide the product manager access to the Amazon CloudWatch dashboard while following the principle of least privilege, a solution architect should create an IAM user specifically for the product manager and attach the CloudWatch Read Only Access managed policy to the user. This policy allows the user to view the dashboard without being able tomake any changes to it. The solution architect should then share the new login credential with the product manager and provide them with the browser URL of the correct dashboard.

VPC endpoint allows you to connect to AWS services using a private network instead of using the public Internet

https://aws.amazon.com/sqs/features/

By routing incoming requests to Amazon SQS, the company can decouple the job requests from the processing instances. This allows them to scale the number of instances based on the size of the queue, providing more resources when needed. Additionally, using an Auto Scaling group based on the queue size will automatically scale the number of instances up or down depending on the workload. Updating the software to read from the queue will allow it to process the job requests in a more efficient manner, improving the performance of the system.

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-capacity-reservations.html

Reserve instances: You will have to pay for the whole term (1 year or 3years) which is not cost effective

https://aws.amazon.com/shield/faqs/

The storage solution that will meet these requirements most cost-effectively is B: Create an S3 Lifecycle configuration to transition objects from S3 Standard to S3 Glacier Deep Archive after 1 month. Amazon S3 Glacier Deep Archive is a secure, durable, and extremely low-cost Amazon S3 storage class for long-term retention of data that is rarely accessed and for which retrieval times of several hours are acceptable. It is the lowest-cost storage option in Amazon S3, making it a cost-effective choice for storing backup files that are not accessed after 1 month. You can use an S3 Lifecycle configuration to automatically transition objects from S3 Standard to S3 Glacier Deep Archive after 1 month. This will minimize the storage costs for the backup files that are not accessed frequently.

Share the existing KMS key with the MSP external account because it has already been used to encrypt the AMI snapshot.https://docs.aws.amazon.com/kms/latest/developerguide/key-policy-modifying-external-accounts.html

Amazon QuickSight is a data visualization service that allows you to create interactive dashboards and reports from various data sources, including Amazon S3 and Amazon RDS for PostgreSQL. You can connect all the data sources and create new datasets in QuickSight, and then publish dashboards to visualize the data. You can also share the dashboards with the appropriate users and groups, and control their access levels using IAM roles and permissions.

https://aws.amazon.com/s3/storage-classes/?trk=66264cd8-3b73-416c-9693-ea7cf4fe846a &sc_channel=ps&s_kwcid=AL!4422!3!536452716950!p!!g!!aws%20s3%20pricing&ef_id=Cj0KCQjwnbmaBhD-ARIsAGTPcfVHUZN5_BMrzl5zBcaC8KnqpnNZvjbZzqPkH6k7q4JcYO5KFLx0YYgaAm6nEALw_wcB:G:s&s_kwcid=AL!4422!3!536452716950!p!!g!!aws%20s3%20pricing

https://docs.aws.amazon.com/kms/latest/developerguide/rotate-keys.html

When you enable automatic key rotation for a customer managed key, AWS KMS generates new cryptographic material for the KMS key every year. AWS KMS also savesthe KMS key's older cryptographic material in perpetuity so it can be used to decrypt data that the KMS key encrypted.

Key rotation in AWS KMS is a cryptographic best practice that is designed to be transparent and easy to use. AWS KMS supports optional automatic key rotation only for customer managed CMKs. Enable and disable key rotation. Automatic key rotation is disabled by default on customer managed CMKs. When you enable (or re-enable) key rotation, AWS KMS automatically rotates the CMK 365 days after the enable date and every 365 days thereafter.

AWS Fargate is a serverless compute engine that lets users run containers without having to manage servers or clusters of Amazon EC2 instances1. Users can use AWS Fargate on Amazon Elastic Container Service (Amazon ECS) to run the containerized web application with Service Auto Scaling. Amazon ECS is a fully managed container orchestration service that supports both Docker and Kubernetes2. Service Auto Scaling is a feature that allows users to adjust the desired number of tasks in an ECS service based on CloudWatch metrics, such as CPU utilization or request count3. Users can use AWS Fargate on Amazon ECS to migrate the application to AWS with minimum code changes and minimum development effort, as they only need to package their application in containers and specify the CPU and memory requirements.

Users can also use an Application Load Balancer to distribute the incoming requests. An Application Load Balancer is a load balancer that operates at the application layer and routes traffic to targets based on the content of the request. Users can register their ECS tasks as targets for an Application Load Balancer and configure listener rules to route requests to different target groups based on path or host headers. Users can use an Application Load Balancer to improve the availability and performance of their web application.

https://aws.amazon.com/blogs/aws/amazon-aurora-fast-database-cloning/

https://aws.amazon.com/kinesis/data-firehose/features/?nc=sn &loc=2#:~:text=into%20Amazon%20S3%2C%20Amazon%20Redshift%2C%20Amazon%20OpenSearch%20Service%2C%20Kinesis,Delivery%20streams

"Typically, you configure buckets to be Requester Pays buckets when you want to share data but not incur charges associated with others accessing the data. For example, you might use Requester Pays buckets when making available large datasets, such as zip code directories, reference data, geospatial information, or web crawling data."https://docs.aws.amazon.com/AmazonS3/latest/userguide/RequesterPaysBuckets.html

https://aws.amazon.com/fsx/lustre/

Amazon FSx has native support for Windows file system features and for the industry-standard Server Message Block (SMB) protocol to access file storage over a network.https://docs.aws.amazon.com/fsx/latest/WindowsGuide/what-is.html

(https://aws.amazon.com/cloudfront

"The Object Lock legal hold operation enables you to place a legal hold on an object version. Like setting a retention period, a legal hold prevents an object version from being overwritten or deleted. However, a legal hold doesn't have an associated retention period and remains in effect until removed."https://docs.aws.amazon.com/AmazonS3/latest/userguide/batch-ops-legal-hold.html

https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html?pg=ln &sec=ft

https://docs.aws.amazon.com/rekognition/latest/dg/a2i-rekognition.html

To protect data in an S3 bucket from accidental deletion, versioning should be enabled, which enables you to preserve, retrieve, and restore every version of every object in an S3 bucket. Additionally, enabling MFA (multi-factor authentication) Delete on the S3 bucket adds an extra layer of protection by requiring an authentication token in addition to the user's access keys to delete objects in the bucket.

https://aws.amazon.com/premiumsupport/knowledge-center/ec2-instance-access-s3-bucket/

AURORA is 5x performance improvement over MySQL on RDS and handles more read requests than write,; maintaining high availability = Multi-AZ deployment

https://docs.aws.amazon.com/lambda/latest/dg/services-rds.htmlhttps://docs.aws.amazon.com/lambda/latest/dg/with-sns.html

To maximize resiliency and scalability, the best solution is to use an Amazon SQS queue as a destination for the jobs. This decouples the primary server from the compute nodes, allowing them to scale independently. This also helps to prevent job loss in the event of a failure. Using an Auto Scaling group of Amazon EC2 instances for the compute nodes allows for automatic scaling based on the workload. In this case, it's recommended to configure the Auto Scaling group based on the size of the Amazon SQS queue, which is a better indicator of the actual workload than the load on the primary server or compute nodes. This approach ensures that the application can handle variable workloads, while also minimizing costs by automatically scaling up or down the compute nodes as needed.

https://aws.amazon.com/blogs/networking-and-content-delivery/scaling-network-traffic-inspection-using-aws-gateway-load-balancer/

"Create an Amazon SQS queue to hold the jobs that needs to be processed. Create an Amazon EC2 Auto Scaling group for the compute application. Set the scaling policy for the Auto Scaling group to add and remove nodes based on the number of items in the SQS queue"

In this case we need to find a durable and loosely coupled solution for storing jobs. Amazon SQS is ideal for this use case and can be configured to use dynamic scalingbased on the number of jobs waiting in the queue.To configure this scaling you can use the backlog per instance metric with the target value being the acceptable backlog per instance to maintain. You can calculate these numbers as follows: Backlog per instance: To calculate your backlog per instance, start with the ApproximateNumberOfMessages queue attribute to determine the length of the SQS queue

https://aws.amazon.com/premiumsupport/knowledge-center/s3-private-connection-no-authentication/

In this scenario, the company wants to avoid regional data transfer charges while downloading and uploading images from Amazon S3. To accomplish this at the lowest cost, the NAT gateway should be launched in each availability zone that the EC2 instances are running in. This allows the EC2 instances to route traffic through the local NAT gateway instead of sending traffic across an availability zone boundary and incurring regional data transfer fees. This method will help reduce the data transfer costs since inter-Availability Zone data transfers in a single region are free of charge.

Moving the catalog to an Amazon Elastic File System (Amazon EFS) file system provides both high availability and durability. Amazon EFS is a fully-managed, highly-available, and durable file system that is built to scale on demand. With Amazon EFS, the catalog data can be storedand accessed from multiple EC2 instances in different availability zones, ensuring high availability. Also, Amazon EFS automatically stores files redundantly within and across multiple availability zones, making it a durable storage option.

Understanding the Requirement: The application needs to write to multiple block storage volumes in multiple EC2 Nitro-based instances simultaneously to achieve higher availability.

Analysis of Options:

General Purpose SSD (gp3) with Multi-Attach: Supports Multi-Attach but does not provide the highest performance required for critical applications.

Throughput Optimized HDD (st1) with Multi-Attach: Not suitable for applications requiring high performance and low latency.

Provisioned IOPS SSD (io2) with Multi-Attach: Provides high performance and durability, suitable for applications requiring simultaneous writes and high availability.

General Purpose SSD (gp2) with Multi-Attach: Similar to gp3 but with less flexibility and performance.

Best Solution:

Provisioned IOPS SSD (io2) with Multi-Attach: This solution ensures the highest performance and availability for the application by allowing multiple EC2 instances to attach to and write to the same EBS volume simultaneously.

Understanding the Requirement: The company needs to mitigate DDoS attacks on its website, which uses AWS Global Accelerator to route traffic to an Application Load Balancer (ALB).

Analysis of Options:

AWS WAF on Global Accelerator: Allows for centralized protection and can block traffic based on rate-based rules, effectively mitigating DDoS attacks with minimal implementation effort.

Lambda Function and VPC Network ACL: Requires custom implementation and ongoing management, increasing complexity and effort.

AWS WAF on ALB: Provides protection but involves additional configuration and management at the ALB level.

CloudFront Distribution in front of Global Accelerator: Adds unnecessary complexity and changes the current traffic flow setup.

Best Solution:

AWS WAF on Global Accelerator: This provides the required protection with the least implementation effort, ensuring effective DDoS mitigation and maintaining the existing architecture.

Understanding the Requirement: EC2 instances need to upload data to S3 without using the public internet, and on-premises servers need to consume this data.

Analysis of Options:

Interface VPC Endpoint for EC2: Not relevant for accessing S3.

Gateway VPC Endpoint for S3 and Direct Connect: Provides private connectivity from EC2 instances to S3 and from on-premises to AWS, ensuring compliance with the requirement to avoid public internet.

Transit Gateway and Site-to-Site VPN: Adds unnecessary complexity and does not provide the same level of performance as Direct Connect.

Proxy EC2 Instances with NAT Gateways: Increases complexity and costs compared to a direct connection using VPC endpoints and Direct Connect.

Best Solution:

Gateway VPC Endpoint for S3 and Direct Connect: This solution ensures secure, private data transfer both within AWS and between on-premises and AWS, meeting the compliance requirements effectively.

Understanding the Requirement: The data science team needs to securely share selective data with the engineering team across multiple AWS accounts with minimal operational overhead.

Analysis of Options:

Copy data to a common account: Involves data duplication and increased storage costs, and requires managing additional permissions.

Lake Formation permissions Grant command: This method can be effective but may involve significant operational overhead if managing permissions across multiple accounts and datasets manually.

AWS Data Exchange: Designed for sharing data externally or between organizations, which adds unnecessary complexity for internal sharing within the same organization.

Lake Formation tag-based access control: Provides a scalable and efficient way to manage access permissions based on tags, allowing fine-grained control and simplified management across accounts.

Best Solution:

Lake Formation tag-based access control: This solution meets the requirements with the least operational overhead, allowing efficient management of cross-account permissions and secure data sharing.

Current Backup Settings: By default, Amazon RDS creates automated backups with a retention period of 7 days.

Regulatory Requirements: The requirement is to retain daily backups for 30 days.

Adjusting Retention Period: You can modify the RDS instance settings to increase the automated backup retention period to 30 days.

Operational Overhead: This solution is the simplest as it leverages existing automated backups and requires minimal intervention.

Implementation: The change can be made via the AWS Management Console, AWS CLI, or AWS SDKs.

References

Amazon RDS Backups:Amazon RDS Documentation

Requirement Analysis: The database experiences peak traffic multiple times a day but has wasted capacity during non-peak hours. The goal is to reduce costs with minimal operational effort.

Aurora Serverless Overview: Aurora Serverless automatically adjusts database capacity based on current demand, scaling up during peak times and scaling down during non-peak times.

Cost Efficiency: Aurora Serverless charges only for the capacity used, which is more cost-effective than provisioning for peak traffic.

Operational Efficiency: Aurora Serverless eliminates the need for manual scaling or scheduling Lambda functions for capacity management.

Implementation: Migrate the database from the provisioned Aurora PostgreSQL cluster to an Aurora Serverless cluster.

References

Amazon Aurora Serverless:Aurora Serverless Documentation

Understanding the Requirement: The company has petabytes of data in S3 Standard across multiple buckets with varying access frequencies. They do not know the access patterns and need a cost-optimized storage solution with minimal operational effort.

Analysis of Options:

S3 Intelligent-Tiering: This storage class automatically moves data between two access tiers (frequent and infrequent) based on changing access patterns. It incurs a small monitoring and automation charge but eliminates the need to manually move data between storage classes.

S3 Storage Class Analysis Tool: While useful for determining access patterns, this tool requires manual intervention to move objects to the appropriate storage class, which increases operational overhead.

S3 Glacier Instant Retrieval: This storage class is designed for data that is rarely accessed but requires instant retrieval when needed. It may not be suitable for data with unknown and varying access patterns.

S3 One Zone-IA: This is a lower-cost option for infrequently accessed data stored in a single availability zone. It does not provide the same level of durability and availability as other options and requires knowledge of access patterns.

Best Option for Operational Efficiency:

S3 Intelligent-Tiering provides the best balance of cost savings and operational efficiency. It dynamically adjusts to access patterns without manual intervention, ensuring the company is only paying for what they need without the risk of incurring high costs for infrequent access data.

Understanding the Requirement: The application is stateful and requires at least two EC2 instances to be running at all times, with a highly available and fault-tolerant architecture.

Analysis of Options:

Minimum capacity of two with instances in separate AZs: Ensures high availability by distributing instances across multiple AZs, fulfilling the requirement of always having two instances running.

Minimum capacity of four: Provides redundancy but is more than what is required and increases cost without additional benefit.

Spot Instances: Not suitable for a stateful application requiring guaranteed availability, as Spot Instances can be terminated at any time.

Combination of On-Demand and Spot Instances: Mixing instance types might provide cost savings but does not ensure the required availability for a stateful application.

Best Solution:

Minimum capacity of two with instances in separate AZs: This setup ensures high availability and meets the requirement with the least cost and complexity.

Understanding the Requirement: The company wants to optimize costs and reduce operational overhead for an RDS for PostgreSQL database that only needs to be active during business hours on weekdays.

Analysis of Options:

Instance Scheduler on AWS: Allows for automated start and stop schedules based on specified times, ideal for resources only needed during certain hours. This directly optimizes costs by running the database only when needed.

Turn off automatic backups and create weekly snapshots: Does not address the requirement of reducing operational overhead and optimizing runtime costs.

Custom Lambda function: This could work but adds unnecessary complexity compared to using the Instance Scheduler.

All Upfront Reserved DB Instances: While this reduces costs, it does not optimize for usage patterns that require the database only during specific hours.

Best Solution:

Instance Scheduler on AWS: This option effectively manages the database runtime based on the specified schedule, reducing costs and operational overhead.

Understanding the Requirement: The development team needs to prevent the launch of large EC2 instances across multiple AWS accounts used for development, staging, and production environments.

Analysis of Options:

IAM Policies: Would need to be applied individually to each user in every account, leading to significant operational overhead.

AWS Resource Access Manager: Used for sharing resources, not for enforcing restrictions on resource creation.

IAM Role in Each Account: Requires creating and managing roles in each account, leading to higher operational overhead compared to using a centralized approach.

Service Control Policy (SCP) with AWS Organizations: Provides a centralized way to enforce policies across multiple AWS accounts, ensuring that large EC2 instances cannot be launched in any account.

Best Solution:

Service Control Policy (SCP) with AWS Organizations: This solution offers the least operational overhead by allowing centralized management and enforcement of policies across all accounts, effectively preventing the launch of large EC2 instances.

AWS Outposts is a fully managed service that delivers AWS infrastructure and services to virtually any on-premises or edge location for a consistent hybrid experience. AWS Outposts supports Amazon EKS, which is a managed service that makes it easy to run Kubernetes on AWS and on-premises. By installing an AWS Outposts rack in the company’s data center, the company can run containers in a Kubernetes environment using Amazon EKS and other AWS managed services, while keeping the data locally in the company’s data center and meeting the compliance requirements. AWS Outposts also provides a seamless connection to the local AWS Region for access to a broad range of AWS services.

Option A is not a valid solution because AWS Local Zones are not deployed in the company’s data center, but in large metropolitan areas closer to end users. AWS Local Zones are owned, managed, and operated by AWS, and they provide low-latency access to the public internet and the local AWS Region. Option B is not a valid solution because AWS Snowmobile is a service that transports exabytes of data to AWS using a 45-foot long ruggedized shipping container pulled by a semi-trailer truck. AWS Snowmobile is not designed for running containers or AWS managed services on-premises, but for large-scale data migration. Option D is not a valid solution because AWS Snowball Edge Storage Optimized is a device that provides 80 TB of HDD or 210 TB of NVMe storage capacity for data transfer and edge computing. AWS Snowball Edge Storage Optimized does not support Amazon EKS or other AWS managed services, and it is not suitable for running containers in a Kubernetes environment.

Understanding the Requirement: The company needs a disaster recovery solution for FSx for NetApp ONTAP in a secondary region, accessible using the same protocols (CIFS and NFS).

Analysis of Options:

Lambda Function and S3: Involves copying data to S3, which changes the access protocols and increases operational overhead.

AWS Backup: Suitable for backup and restore but not for real-time or near-real-time replication for disaster recovery.

FSx for ONTAP with SnapMirror: SnapMirror provides efficient replication between ONTAP instances, maintaining access protocols and requiring minimal operational overhead.

Amazon EFS: Does not support CIFS and requires migrating data, increasing complexity and changing access protocols.

Best Solution:

FSx for ONTAP with SnapMirror: This solution ensures seamless disaster recovery with the same access protocols and minimal operational overhead.

Requirement Analysis: The product catalog search is causing high CPU utilization on the MySQL DB instance, slowing down the website.

ElastiCache Overview: Amazon ElastiCache for Redis can be used to cache frequently accessed data, reducing load on the database.

Lazy Loading: This caching strategy loads data into the cache only when it is requested, improving response times for repeated queries.

Implementation:

Set up an ElastiCache for Redis cluster.

Modify the application to check the cache before querying the database.

Use lazy loading to populate the cache on cache misses.

Conclusion: This approach reduces database load and improves website performance during product catalog searches.

References

Amazon ElastiCache:ElastiCache Documentation

Caching Strategies:ElastiCache Caching Strategies

Requirement Analysis: The application must process each credit card data validation request at least once, securely and cost-effectively.

SQS FIFO Queues: Ensures that each message is processed exactly once and in the exact order sent.

AWS Lambda: Using Lambda for event-driven processing ensures scalability and cost-efficiency.

SSE-SQS: Provides encryption at rest using SQS-managed keys, simplifying encryption management.

Implementation:

Set up SQS FIFO queues as the event source for Lambda.

Enable SSE-SQS for encryption.

Ensure the Lambda execution role has the necessary permissions to use the encryption keys.

Conclusion: This combination meets the requirements of security, exact-once processing, and cost-effectiveness.

References

Amazon SQS:Amazon SQS Documentation

AWS Lambda with SQS:Using AWS Lambda with Amazon SQS

Requirement Analysis: Need quick, cost-effective, and consistent access to on-premises network services from multiple AWS accounts.

AWS Transit Gateway: Centralizes and simplifies network management by connecting VPCs and on-premises networks.

Direct Connect Integration: Assigning DX to the transit gateway ensures consistent and high-performance connectivity.

Operational Overhead: Minimal because Transit Gateway simplifies routing and management.

Implementation:

Set up AWS Transit Gateway.

Connect new AWS accounts to the Transit Gateway.

Route traffic through Transit Gateway to on-premises servers via Direct Connect.

Conclusion: This solution provides a scalable, cost-effective, and low-overhead method to meet connectivity requirements.

References

AWS Transit Gateway:AWS Transit Gateway Documentation

Understanding the Requirement: The company needs to monitor and notify changes to the OU hierarchy with minimal operational overhead.

Analysis of Options:

AWS Control Tower with Account Drift Notifications: AWS Control Tower provides automated account provisioning and governance, including drift detection and notifications for changes in the OU hierarchy.

AWS Control Tower with AWS Config: AWS Config provides resource configuration tracking but is more complex compared to drift notifications directly available in Control Tower.

AWS Service Catalog with CloudTrail: While CloudTrail tracks changes, setting up notification mechanisms involves more operational overhead.

AWS CloudFormation with Drift Detection: Suitable for tracking configuration changes but less efficient for monitoring OU hierarchy changes compared to Control Tower's built-in features.

Best Solution:

AWS Control Tower with Account Drift Notifications: Provides a streamlined and efficient way to detect and notify changes in the OU hierarchy with minimal operational overhead.

Requirement Analysis: The company wants to identify cost optimizations for EC2 instances, the Auto Scaling group, and EBS volumes with high operational efficiency.

AWS Compute Optimizer: This service provides actionable recommendations to help optimize your AWS resources, including EC2 instances, Auto Scaling groups, and EBS volumes.

Cost Recommendations: Compute Optimizer analyzes the utilization of resources and provides specific recommendations for rightsizing or optimizing the configurations.

Operational Efficiency: Using Compute Optimizer automates the process of identifying cost-saving opportunities, reducing the need for manual analysis.

Implementation:

Enable AWS Compute Optimizer for your AWS account.

Review the recommendations provided for EC2 instances, Auto Scaling groups, and EBS volumes.

Conclusion: This solution provides a comprehensive, automated approach to identifying cost optimizations with minimal operational effort.

References

AWS Compute Optimizer:AWS Compute Optimizer Documentation

Understanding the Requirement: The company needs to automate inventory and updates of diverse OS versions on EC2 instances and summarize common vulnerabilities for monthly reviews.

Analysis of Options:

Systems Manager Patch Manager and Security Hub: Patch Manager automates patching, but Security Hub is more focused on compliance and security posture rather than inventory and vulnerability management.

Systems Manager Patch Manager and Amazon Inspector: Patch Manager automates OS updates, and Amazon Inspector provides vulnerability assessments, making this a comprehensive solution for the requirements.

AWS Shield Advanced and AWS Config: Shield Advanced is for DDoS protection, not suitable for OS patch management and vulnerability reporting.

Amazon GuardDuty and AWS Config: GuardDuty is for threat detection and monitoring, not specifically for patch management and vulnerability assessments.

Best Solution:

Systems Manager Patch Manager and Amazon Inspector: This combination automates OS updates and provides detailed vulnerability assessments, meeting both the inventory and security reporting needs effectively.

Understanding the Requirement: The application running in a private subnet needs to store and retrieve data from S3 without data traveling over the public internet.

Analysis of Options:

NAT Gateway: Allows private subnets to access the internet but incurs additional costs and still routes traffic through the public internet.

AWS Storage Gateway: Provides hybrid cloud storage solutions but is not the most cost-effective for direct S3 access from within the VPC.

S3 Interface Endpoint: Provides private access to S3 but is generally used for specific use cases where more granular control is required, which might be overkill and more expensive.

S3 Gateway Endpoint: Provides private, cost-effective access to S3 from within the VPC without routing traffic through the public internet.

Best Solution:

S3 Gateway Endpoint: This option meets the requirements for secure, private access to S3 from a private subnet most cost-effectively.

Understanding the Requirement: The company needs programmatic access to its AWS usage costs for the current year and cost forecasts for the next 12 months, with minimal operational overhead.

Analysis of Options:

AWS Cost Explorer API: Provides programmatic access to detailed usage and cost data, including forecast costs. It supports pagination for handling large datasets, making it an efficient solution.

Downloadable AWS Cost Explorer report csv files: While useful, this method requires manual handling of files and does not provide real-time access.

AWS Budgets actions via FTP: This is less suitable as it involves setting up FTP transfers and does not provide the same level of detail and real-time access as the API.

AWS Budgets reports via SMTP: Similar to FTP, this method involves additional setup and lacks the real-time access and detail provided by the API.

Best Option for Minimal Operational Overhead:

AWS Cost Explorer API provides direct, programmatic access to cost data, including detailed usage and forecasting, with minimal setup and operational effort. It is the most efficient solution for integrating cost data into an operational cost dashboard.

Understanding the Requirement: The application running on EC2 instances in a private subnet needs to process sensitive information from an S3 bucket without using the internet.

Analysis of Options:

Internet Gateway: This would expose the application to the internet, which is not suitable for accessing sensitive information securely.

VPN Connection: VPN is primarily used for secure connections between on-premises networks and AWS VPCs, not for direct S3 access within the same VPC.

NAT Gateway: This allows instances in a private subnet to connect to the internet, but the goal is to avoid internet access.

VPC Endpoint: Provides a private connection between the VPC and S3 without using the internet, ensuring secure access to the S3 bucket.

Best Solution:

VPC Endpoint: Configuring a VPC endpoint allows secure, private communication between the EC2 instances and the S3 bucket without using the internet, ensuring data security and compliance.

Understanding the Requirement: The company needs to copy files generated by an on-premises application to AWS without modifying the application code. The files are stored on an SMB file share and require a low-latency connection to the application servers.

Analysis of Options:

Amazon Elastic File System (EFS): EFS is designed for Linux-based workloads and does not natively support SMB file shares.

Amazon FSx for Windows File Server: FSx supports SMB file shares but would require changes to the application or additional infrastructure to connect on-premises systems.

AWS Snowball: Suitable for large data transfers but not for continuous, low-latency file copying.

AWS Storage Gateway: Provides a hybrid cloud storage solution, supporting SMB file shares and enabling seamless copying of files to AWS without requiring changes to the application.

Best Solution:

AWS Storage Gateway: This service meets the requirement for a low-latency, seamless file transfer solution from on-premises to AWS without modifying the application code.

Understanding the Requirement: Senior leadership wants a custom dashboard displaying NAT gateway costs daily, starting from the beginning of the current month.

Analysis of Options:

QuickSight with DataSync: While QuickSight is suitable for dashboards, DataSync is not designed for querying and analyzing data reports.

QuickSight with Athena: QuickSight can visualize data queried by Athena, which is designed to analyze data directly from S3.

CloudWatch with DataSync: CloudWatch is primarily for monitoring metrics, not for creating detailed cost analysis dashboards.

CloudWatch with Athena: Similarly, using CloudWatch with Athena does not align well with the requirement for a visual dashboard.

Best Solution for Visualization and Querying:

Amazon QuickSight with Athena: This combination allows for powerful data visualization and querying capabilities. QuickSight can create dynamic dashboards, while Athena efficiently queries the cost and usage report data stored in S3.

Understanding the Requirement: The university wants to centralize management and automate backups for its AWS services (EC2, RDS, and DynamoDB), reducing reliance on custom scripts.

Analysis of Options:

Third-party backup software with AWS Storage Gateway: This solution introduces external dependencies and adds complexity compared to using native AWS services.

AWS Backup: Provides a centralized, fully managed service to automate and manage backups across various AWS services, including EC2, RDS, and DynamoDB.

AWS Config: Primarily used for compliance and configuration monitoring, not for backup management.

AWS Systems Manager State Manager: Useful for configuration management but not specifically designed for managing backups.

Best Solution:

AWS Backup: This service offers the necessary functionality to centralize and automate backups, providing a streamlined and integrated solution with minimal effort.

Understanding the Requirement: Developers in the Development account need to push code changes to the Production account, with phased access control for different stages of the project.

Analysis of Options:

Policy Documents in Each Account: This approach increases complexity and is harder to manage compared to role-based access.

IAM Role in Development Account: Roles in the Development account cannot directly control access to resources in the Production account.

IAM Role in Production Account: Creating a role in the Production account with a trust policy that allows the Development account to assume it provides controlled, secure access.

IAM Group in Production Account: This approach does not provide the required cross-account access control.

Best Solution:

IAM Role in the Production Account: This method allows precise control over who can access the Production account from the Development account, with the ability to manage permissions and access levels effectively.

Understanding the Requirement: The company needs to migrate sensitive and critical data from on-premises SQL Server databases to AWS, aiming to increase security and reduce operational overhead.

Analysis of Options:

EC2 Instances with KMS: Running SQL Server on EC2 provides control but requires significant operational overhead for management, backups, patching, and high availability.

Multi-AZ Amazon RDS for SQL Server with KMS: Amazon RDS for SQL Server offers managed database services, reducing operational overhead. Multi-AZ deployment provides high availability, and KMS encryption ensures data security.

Amazon S3 and Macie: S3 is not a suitable replacement for relational databases, and Macie is used for data security and compliance but not for database operations.

Amazon DynamoDB and CloudWatch Logs: DynamoDB is a NoSQL database and does not support SQL Server workloads directly. CloudWatch Logs are used for monitoring, not for ensuring database security.

Best Solution:

Multi-AZ Amazon RDS for SQL Server with KMS: This solution meets the requirements for security, high availability, and reduced operational overhead by using a managed database service with encryption.

Understanding the Requirement: The company needs to map its IT infrastructure to identify and enforce security policies, with the ability to quickly query and identify security risks.

Analysis of Options:

Amazon RDS: While suitable for relational data, it is not optimized for handling complex relationships and querying those relationships, which is essential for an IT infrastructure map.

Amazon Neptune: A graph database service designed for handling highly connected data. It uses SPARQL to query graph data efficiently, making it ideal for mapping IT infrastructure and identifying relationships that pose security risks.

Amazon Redshift: A data warehouse solution optimized for complex queries on large datasets but not specifically for graph data.

Amazon DynamoDB: A NoSQL database that uses PartiQL for querying, but it is not optimized for complex relationships in graph data.

Best Option for Mapping and Querying IT Infrastructure:

Amazon Neptune provides the most suitable solution with the least operational overhead. It is purpose-built for graph data and enables efficient querying of complex relationships to identify security risks.

Understanding the Requirement: The company wants to migrate an application to AWS, increase its availability, and use AWS WAF in the architecture.

Analysis of Options:

Auto Scaling group with ALB and WAF: This option provides high availability by distributing instances across multiple Availability Zones. The ALB ensures even traffic distribution, and AWS WAF provides security at the application layer.

Cluster placement group with ALB and WAF: Cluster placement groups are for low-latency networking within a single AZ, which does not provide the high availability across AZs.

Two EC2 instances with ALB and WAF: This setup provides some availability but does not scale automatically, missing the benefits of an Auto Scaling group.

Auto Scaling group with WAF directly: AWS WAF cannot be directly connected to an Auto Scaling group; it needs to be attached to an ALB, CloudFront distribution, or API Gateway.

Best Solution:

Auto Scaling group with ALB and WAF: This configuration ensures high availability, scalability, and security, meeting all the requirements effectively.