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

This solution usesCloudFrontto serve the website securely over HTTPS usingAWS Certificate Manager (ACM)for SSL certificates.Origin Access Control (OAC)ensures that only CloudFront can access the S3 bucket directly.AWS WAFwith an IP set rule restricts access to the website, allowing only the on-premises IP address.Route 53is used to create an alias record pointing to the CloudFront distribution. This setup ensures secure, private access to the website with low administrative overhead.

Option A and B: S3 bucket policies and access points do not provide HTTPS support, nor do they offer the same level of security as CloudFront with WAF.

Option D: Signed URLs are more suitable for temporary, expiring access rather than a permanent solution for on-premises employees.

AWS References:

Amazon CloudFront with Origin Access Control

AWS Step Functions is designed to coordinate multiple AWS services into serverless workflows, handling errors, retries, and complex logic. By configuring Amazon S3 Event Notifications to trigger an Amazon EventBridge rule, you can automatically start a Step Functions workflow when a new object is uploaded to S3. Step Functions can manage retries for failed uploads and orchestrate calls to various AWS services with minimal operational effort.

AWS Documentation Extract:

“You can use Amazon EventBridge to initiate AWS Step Functions workflows in response to events from S3 buckets. Step Functions can orchestrate the sequence of AWS service calls and automatically handle retries and errors, making it ideal for automating and managing complex workflows.”

(Source: AWS Step Functions documentation, Using Step Functions with EventBridge and S3)

Other options:

A: Requires manual implementation of orchestration and error handling.

B & C: Involve additional infrastructure management and custom logic for workflow orchestration and error handling, increasing operational effort.

For predictable workloads requiring a relational database, Amazon RDS for MySQL offers a managed, cost-effective solution. Storing product images in Amazon S3 is highly durable and cost-efficient, especially for static assets like images. This combination leverages managed services to reduce operational overhead and costs.

AWS VPC endpoints (interface and gateway) allow private connectivity from VPC resources to AWS services without requiring public IP addresses or internet gateways. This ensures applications remain isolated in private subnets while securely accessing AWS services. NAT gateways (B) would allow internet access, which does not meet the security requirement. Internet gateways (C) directly expose traffic to the internet, which violates the isolation requirement. Direct Connect (D) connects on-premises environments to AWS but does not provide service access from private subnets. Therefore, option A — using interface VPC endpoints — is the correct solution.

The optimal solution for scalable and resilient hybrid networking is to use AWS Direct Connect with a Direct Connect gateway for secure, low-latency access to AWS, and an AWS Transit Gateway to manage connectivity among hundreds of VPCs.

By associating the Transit Gateway with the Direct Connect gateway, you enable transitive routing between on-premises and all VPCs, while minimizing network complexity and maintaining high performance.

VPC peering does not scale well, and VPNs don’t offer the same performance or consistency.

Amazon S3 Intelligent-Tiering automatically moves objects between two access tiers based on changing access patterns. Objects not accessed for 30 days move to a lower-cost tier, but are still immediately available with millisecond retrieval. If objects become frequently accessed again, they are moved back to the frequent access tier. There are no retrieval charges and no impact on availability or performance. This storage class is specifically designed for unpredictable access patterns and cost optimization, requiring minimal management.

AWS Documentation Extract:

"S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable."

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

B: Glacier Deep Archive is for archival, not for low-latency millisecond access.

C: EFS is not optimized for object storage or global CDN distribution.

D: Cache-Control only affects CloudFront or browser caching, not S3 storage cost.

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.

Partition Key Issue:

Using "service name" as the partition key results in uneven data distribution. Some shards may become hot due to excessive logs from certain services, leading to throttling errors.

Changing the partition key to "creation timestamp" ensures a more even distribution of records across shards.

Incorrect Options Analysis:

Option A: On-demand capacity mode eliminates throughput management but is more expensive and does not address the root cause.

Option B: Adding more shards does not solve the issue if the partition key still creates hot shards.

Option D: Using separate streams increases complexity and is unnecessary.

Key Requirements:

High availability and automatic recovery.

Separate transactional and analytical queries with minimal performance impact.

Allow up to a 4-hour lag for analytical queries.

Analysis of Options:

Option A:

Multi-AZ deployments provide high availability but do not include read replicas for separating transactional and analytical queries.

Analytical queries on the secondary DB instance would impact the transactional workload.

Incorrect Approach:Does not meet the requirement of query separation.

Option B:

Multi-AZ DB clusters provide high availability and include a reader endpoint. However, these are better suited for Aurora and not RDS for MySQL.

Incorrect Approach:Not applicable to standard RDS for MySQL.

Option C:

Multiple read replicas allow separation of transactional and analytical workloads.

Queries can be pointed to a replica in a different AZ, ensuring no impact on transactional queries.

Correct Approach:Meets all requirements with high availability and query separation.

Option D:

Creating nightly snapshots and read-only databases adds significant operational overhead and does not support the 4-hour lag requirement.

Incorrect Approach:Not practical for dynamic query separation.

AWS Solution Architect References:

Amazon RDS Read Replicas

Multi-AZ Deployments

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

Why Option C is Correct:

NAT Gateway: Allows private subnets to access the internet for outbound requests while preventing inbound connections.

High Availability: Deploying NAT gateways in both AZs ensures fault tolerance.

Shared Route Table: Simplifies routing configuration for private subnets.

Why Other Options Are Not Ideal:

Option A: Creating separate route tables for each subnet adds unnecessary complexity.

Option B: Internet gateways allow inbound access, violating the requirement to block public IPv4 access.

Option D: Egress-only internet gateways are designed for IPv6, not IPv4.

AWS References:

Amazon VPC NAT Gateway:AWS Documentation - NAT Gateway

This solution is the most scalable and efficient way to handle large volumes of survey data while automating sentiment analysis:

API Gateway and SQS: The survey results are sent to API Gateway, which forwards the data to an SQS queue. SQS can handle large volumes of messages and ensures that messages are not lost.

AWS Lambda: Lambda is triggered by polling the SQS queue, where it processes the survey data.

Amazon Comprehend: Comprehend is used for sentiment analysis, providing insights into customer satisfaction.

DynamoDB with TTL: Results are stored in DynamoDB with aTime to Live (TTL)attribute set to expire after 365 days, automatically removing old data and reducing storage costs.

Option B (EC2 API): Running an API on EC2 requires more maintenance and scalability management compared to API Gateway.

Option C (S3 and Rekognition): Amazon Rekognition is for image and video analysis, not sentiment analysis.

Option D (Amazon Lex): Amazon Lex is used for building conversational interfaces, not sentiment analysis.

AWS References:

Amazon Comprehend for Sentiment Analysis

Amazon SQS

DynamoDB TTL

Amazon S3 Glacier Flexible Retrieval is a low-cost archival storage class that supports retrieval of data within minutes (expedited access ~1–5 minutes), making it ideal for audit scenarios where occasional, quick access to archived data is required. In contrast, Glacier Deep Archive takes hours to retrieve.

Using SQS as a buffer between S3 and the Lambda function ensures durability and allows for retries in case of processing failures. Messages in the queue can be retried by Lambda, and failed processing can be directed to a dead-letter queue for further inspection. This guarantees reliable and scalable message-driven processing.

State Machine Testing with Logs:

Changing the log level to ALL enables capturing detailed request and response data. This helps verify HTTP headers, body, and responses.

Incorrect Options Analysis:

Option A and B: The TestState API is not a valid option for Step Functions.

Option C: A data flow simulator does not exist for AWS Step Functions.

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.

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

Provisioning the Endpoint:

Navigate to the VPC Dashboard.

Select "Endpoints" and create a new endpoint.

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

Select the appropriate VPC and subnets.

Adjust the route tables of the subnets to include the new endpoint.

Update Route Tables: Modify the route tables of the subnets to direct traffic destined for S3 to the newly created endpoint. This ensures that traffic to S3 does not go through the NAT instance, avoiding the saturated network and eliminating timeouts.

Operational Efficiency: This solution minimizes operational overhead by removing dependency on the NAT instance and avoiding internet traffic, leading to more stable and secure S3 interactions.

Amazon Kinesis Data Streams is designed for low-latency ingestion of streaming data. Combined with Amazon Managed Service for Apache Flink, telemetry can be processed and aggregated in near real time. Processed metrics can be sent to Amazon CloudWatch, which natively supports creating dashboards, metrics visualization, and alarms. Firehose (B) is primarily for batch ingestion and delivery, not real-time analytics. EMR with Athena (C) introduces more complexity and is better for large-scale offline analytics. DynamoDB Streams (D) is not a fit because telemetry data is not stored in DynamoDB. Therefore, option A provides the most suitable and real-time analytics pipeline for telemetry data.

AWS Fargate provides per-pod isolation for CPU, memory, storage, and networking, making it ideal for multi-tenant use cases.

AWS Documentation References:

EKS with Fargate

AWS Compute Optimizerprovides detailed recommendations for optimizingAmazon EBS volumes, including insights into underutilized volumes, inefficient configurations, and potential cost savings. It analyzes usage patterns and provides recommendations based on historical data, making it the ideal tool for this use case.

Option A (Amazon Inspector): Amazon Inspector is for security assessments, not for cost optimization.

Option B (Systems Manager): Systems Manager does not specifically provide EBS optimization recommendations.

Option C (CloudWatch): CloudWatch metrics help monitor usage but do not offer optimization recommendations like Compute Optimizer.

AWS References:

AWS Compute Optimizer

To allocate costs based on team ownership, AWS recommends tagging resources using cost allocation tags. Activating the “owner” tag as a cost allocation tag allows AWS Cost Explorer to categorize and group spending. Additionally, filtering for Amazon ECS services enables the visibility into service-specific usage. This approach is both scalable and operationally efficient.

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.

Comprehensive and Detailed Step-by-Step Explanation:

The requirement is toprevent data deletion by any user, including administrators, for 7 years while allowing automatic deletion afterward.

S3 Object Lock in Compliance Mode (Correct Choice - C)

Compliance mode ensures that even the root user cannot delete or modify the objects during the retention period.

After 7 years, the S3 Lifecycle policy automatically deletes the objects.

This meets bothimmutability and automatic deletionrequirements.

Governance Mode (Option B - Incorrect)

Governance mode prevents deletion,but administrators can override it.

The requirement explicitly states thateven administrators must not be able to delete the data.

S3 Bucket Policy (Option A - Incorrect)

An S3 bucket policy candeny deletes, but policies can be modified at any time by administrators.

It does not enforce strict retention like Object Lock.

S3 Batch Operations Job (Option D - Incorrect)

A legal hold does not have an automatic expiration.

Legal holds must be manually removed, which is not efficient.

Why Option C is Correct:

S3 Object Lock in Compliance Mode prevents deletion by all users, including administrators.

The S3 Lifecycle policy deletes the data automatically after 7 years, reducing operational overhead.

Amazon RDS does not support enabling encryption at rest on an existing unencrypted DB instance. To encrypt an existing RDS instance’s data at rest, the recommended method is to:

Take a snapshot of the unencrypted DB instance.

Create an encrypted copy of the snapshot using AWS KMS. This encrypted snapshot contains the existing data encrypted at rest.

Restore a new DB instance from the encrypted snapshot. This new instance will have encryption at rest enabled.

Additionally, to manage encryption keys securely, companies can use customer managed keys (CMKs) in AWS Key Management Service (KMS). CMKs provide greater control over key management policies, rotation, and usage permissions compared to default AWS managed keys. Using a CMK allows customization of access control and auditability.

Option A is incorrect because you cannot enable encryption directly on a snapshot; you must create an encrypted copy. Option C is invalid because encryption cannot be enabled by modifying an existing instance’s configuration. Option D refers to the default AWS managed key, which is less flexible than customer managed keys.

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.

Apresigned URLallows temporary access to a specific object in an S3 bucket without needing to make the bucket public or creating and managing additional IAM users. The URL is time-limited, and permissions are granted only to the specific object (in this case, the annual report), making it a highly secure and operationally efficient solution.

With a presigned URL, the consultant can access the report for the specified duration (7 days), after which the URL will expire automatically, removing the need for manual intervention to revoke access.

AWS References:

Amazon S3 Presigned URLsexplain how to generate a presigned URL to grant temporary access to S3 objects.

Best Practices for S3 Securityemphasize using presigned URLs for sharing temporary access to S3 objects securely.

Why the other options are incorrect:

A. Public static website: This approach involves making the S3 bucket publicly accessible, which is unnecessary and insecure for sensitive data.

B. Enable public access: Granting public access to the entire bucket, even temporarily, is a security risk and violates best practices.

C. Create an IAM user: Creating an IAM user and managing credentials is unnecessary overhead and less secure compared to a presigned URL for this short-term need.

Amazon RDS for MySQL Reserved Instances provide significant savings over on-demand pricing when you plan to run the database for long periods. This is the most cost-effective option for non-production, long-running managed MySQL workloads.

Reference Extract:

"Reserved Instances provide a significant discount compared to On-Demand pricing and are recommended for steady-state workloads that run for an extended period."

Source: AWS Certified Solutions Architect – Official Study Guide, RDS Cost Optimization section.

AWS Backup is a fully managed backup service designed to centralize and automate data protection across AWS services including EC2 and RDS. It allows users to define backup schedules (backup plans) and automatically create and retain backups. AWS Backup also offers restore testing plans, allowing users to automate the validation of backups by restoring them in a controlled manner. This service is built to minimize operational overhead by removing the need to manage custom scripts, manual processes, or additional orchestration services. This aligns with AWS best practices for resilience, automation, and operational excellence.

Reference Extract from AWS Documentation / Study Guide:

"AWS Backup enables you to centralize and automate data protection across AWS services. You can create backup plans, schedule backups, and set lifecycle policies. AWS Backup also enables restore testing to verify your backup integrity, with minimal manual intervention."

Source: AWS Certified Solutions Architect – Official Study Guide, Resiliency and Disaster Recovery section; AWS Backup User Guide.

To run RDS instances only during business hours with the least operational overhead, you can useAmazon EventBridgeto schedule events that invokeAWS Lambda functions. The Lambda functions can be configured to start and stop the RDS instances based on the specified schedule (business hours). EventBridge rules allow you to define recurring events easily, and Lambda functions provide a serverless way to manage RDS instance start and stop operations, reducing administrative overhead.

Option A: While CloudWatch alarms could be used, they are more suited for monitoring, and using Lambda with EventBridge is simpler.

Option B (Trusted Advisor): Trusted Advisor is not ideal for scheduling tasks.

Option C (Systems Manager): Systems Manager could also work, but EventBridge and Lambda offer a more streamlined and lower-overhead solution.

AWS References:

Amazon EventBridge Scheduler

AWS Lambda

This is a classic use case for Amazon Kinesis Data Streams + OpenSearch + QuickSight:

Kinesis Data Streams: For real-time ingestion and processing

OpenSearch Service: For fast full-text search, indexing, and analysis

QuickSight: For rich dashboard visualizations

This stack is fully managed, scalable, and native to AWS, minimizing operational overhead.

Amazon Timestream: Purpose-built time series database optimized for telemetry and IoT data ingestion and analytics.

Amazon SageMaker: Provides ML capabilities for predictive maintenance workflows.

Amazon QuickSight: Efficiently generates interactive, real-time visual reports from Timestream data.

Optimized for Scale: Timestream efficiently handles large-scale telemetry data with time-series indexing and queries.

Amazon Timestream Documentation

Why Option C is Correct:

S3 Access Points: Provide scalable management of access to large datasets with specific permissions for individual prefixes.

Dynamic Prefixes: Access points simplify managing access to a growing number of prefixes without relying solely on a single bucket policy.

Fine-Grained Control: Resource-based permissions on access points enforce prefix-level isolation effectively.

Why Other Options Are Not Ideal:

Option A: Using deny/allow bucket policies introduces complexity and is less scalable for dynamic prefixes.

Option B: Encryption ensures data security but does not address access management.

Option D: Pre-signed URLs are temporary and not suitable for managing access at scale.

AWS References:

Amazon S3 Access Points:AWS Documentation - S3 Access Points

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.

Amazon CloudFront is a content delivery network (CDN) service that distributes content with low latency and high transfer speeds. Placing CloudFront in front of the S3 bucket ensures globalusers download content from the nearest edge location, reducing latency significantly.

Edge-Optimized API: Designed for global users by routing requests through CloudFront's edge locations, reducing latency.

Content Encoding: Enabling content encoding compresses data, further optimizing performance by decreasing payload size.

Caching: Adding API Gateway caching reduces the number of calls to Lambda and database backends, improving latency.

Reserved Concurrency: Although useful, this does not directly affect latency for transferring static and dynamic content.

AWS API Gateway Edge-Optimized APIs Documentation

AWS Application Migration Service (AWS MGN) is the recommended tool for a lift-and-shift strategy, especially for time-sensitive migrations. It automates the replication of on-premises VMs to AWS, minimizing the effort required for migration and testing.

Key steps:

Replication with AWS MGN: The AWS Replication Agent is installed on the VMs to continuously replicate data to AWS, allowing you to manage migration easily.

Testing and Cutover: Initial replication allows for testing in AWS before performing the final cutover, ensuring that the migration process is smooth and data integrity is maintained.

AWS Documentation: AWS MGN is recommended for migrating virtual machines to the cloud with minimal downtime and disruption​​.

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.

To protect against a Regional failure, the application must be deployed in multiple Regions. Amazon Route 53 DNS failover with health checks allows traffic to be automatically routed to a healthy Region when the primary becomes unavailable, meeting high availability and disaster recovery requirements.

Background Jobs with Event Invocation Type:

The Event invocation type is asynchronous, meaning the Lambda function does not send an immediate result to the API Gateway and processes the request in the background. This is ideal for video encoding tasks that take time.

REST API vs. HTTP API:

REST APIs support advanced features like API keys, request validation, and throttling that HTTP APIs do not support fully.

Since the developer needs API keys and request validations, a REST API is the correct choice.

Integration with Lambda:

AWS Lambda integration is seamless with REST APIs, and using the Event invocation ensures asynchronous processing.

Incorrect Options Analysis:

Option A: HTTP API lacks full support for API keys and validation.

Option CandD: RequestResponse invocation type requires immediate responses, unsuitable for background jobs.

AWS advises using serverless event-driven processing to minimize operational burden and scale automatically with demand. Amazon SQS can be directly configured as an event source for AWS Lambda. With this setup, Lambda polls the queue, invokes the function, and processes messages without requiring the customer to manage infrastructure. Scaling is automatic, based on the volume of messages in the queue. Option A (increasing instance size) still leaves scaling and management challenges. Option B reduces cost when idle but does not address scaling. Option D requires manual triggering and does not meet continuous processing requirements. Migrating the script to Lambda (C) fully eliminates the need for instance management, providing scalability, reliability, and reduced operational overhead.

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.

Amazon S3 Standard provides virtually unlimited scalability, high durability (11 nines), and millisecond latency. It is designed for storing large volumes of unstructured content such as media files. S3 also supports pre-signed URLs and direct browser uploads, enabling users to upload files securely without passing through backend servers. EBS volumes (A) are block storage, limited to single AZ, and not suitable for web-scale storage. EFS (B) is a shared file system for POSIX workloads, not for direct browser uploads. S3 Express One Zone (D) offers higher performance for small objects but does not provide cross-AZ durability, making it unsuitable for growing global content. Therefore, option C is the most scalable, durable, and cost-effective solution.

The requirement is to route users to the nearest AWS Region where the application is deployed. The best solution is to use Amazon Route 53 with a geolocation routing policy, which routes traffic based on the geographic location of the user making the request.

Geolocation Routing: This routing policy ensures that users are directed to the resources (in this case, EC2 instances) that are geographically closest to them, thereby reducing latency and improving the user experience.

Application Load Balancer (ALB): Within each Region, an internet-facing Application Load Balancer (ALB) is used to distribute incoming traffic across multiple EC2 instances in different Availability Zones. ALBs are designed to handle HTTP/HTTPS traffic and provide advanced features like content-based routing, SSL termination, and user authentication.

Why Not Other Options?:

Option B (Geoproximity + NLB): Geoproximity routing is similar but more complex as it requires fine-tuning the proximity settings. A Network Load Balancer (NLB) is better suited for TCP/UDP traffic rather than HTTP/HTTPS.

Option C (Multivalue Answer Routing + ALB): Multivalue answer routing does not direct traffic based on user location but rather returns multiple values and lets the client choose. This does not meet the requirement for geographically routing users.

Option D (Weighted Routing + NLB): Weighted routing splits traffic based on predefined weights and does not consider the user's geographic location. NLB is not ideal for this scenario due to its focus on lower-level protocols.

AWS References:

Amazon Route 53 Routing Policies- Detailed explanation of the various routing policies available in Route 53, including geolocation.

Elastic Load Balancing- Information on the different types of load balancers in AWS and when to use them.

Amazon Timestreamis purpose-built for storing and analyzing time-series data like telemetry.

Option Aleverages Timestream, SageMaker for ML, and QuickSight for visualization, meeting all requirements with minimal complexity.

Option Binvolves more complex DynamoDB-EMR integration.

Option Cuses Neptune, which is designed for graph databases, not telemetry data.

Option Dincorrectly uses Athena for visualization instead of QuickSight.

Since the workload is interruptible and can resume after restarts, Amazon EC2 Spot Instances are the most cost-effective option, offering savings of up to 90% compared to On-Demand. Running the batch workload on AWS Batch with Spot Instances allows automatic job queue management, interruption handling, and scheduling. Option A and C reduce cost but still rely on reserved or committed pricing for On-Demand capacity. Option B (Capacity Reservations) increases cost instead of reducing it. Therefore, the most cost-optimized solution is D — AWS Batch with EC2 Spot Instances.

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.

Amazon RDS read replicas provide a way to offload read traffic from the primary database, allowing read-intensive applications to query the replica without impacting the performance of the production (write) database. This is especially effective for workloads that involve frequently changing data but do not benefit from caching, since queries are rarely repeated.

Reference Extract from AWS Documentation / Study Guide:

"Read replicas allow you to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads."

Source: AWS Certified Solutions Architect – Official Study Guide, RDS Read Replica section.

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.

Amazon OpenSearch Service is the AWS-native service for real-time search, analytics, and visualization. Ingesting streaming data with Amazon Kinesis Data Streams ensures scalable and reliable data ingestion. OpenSearch supports indexing and querying the data in near real time. QuickSight can then be integrated for visualization and reporting. Options A and B involve batch processing and are not optimized for real-time search. Option C (EKS + DynamoDB) is not a fit for search workloads, as DynamoDB does not support advanced text search. Option D provides the closest AWS-native equivalent to the company’s existing search-and-visualization architecture.

Detailed Explanation:

A. ECS + API Gateway:Overly complex and costly for an on-demand, intermittent workload.

B. EventBridge + SNS:EventBridge schedules are unnecessary for on-demand generation.

C. EKS + API Gateway:Overkill for this use case, with high operational overhead.

D. Lambda + SES:Most cost-effective and efficient solution for generating and emailing reports on demand.

This question centers on improving scalability and global access performance.

Auto Scaling groups enable EC2 instances to scale dynamically in response to demand, ensuring availability during peak hours without manual intervention. Amazon RDS read replicas offload read traffic, improving read throughput and reducing latency on the primary database instance. Deploying Amazon CloudFront with S3 as origin accelerates delivery of static transaction documents globally by caching content at edge locations, reducing latency for users worldwide.

Option B focuses on vertical scaling (larger instances) and caching with ElastiCache, but it does not address global content delivery optimally. AWS Global Accelerator accelerates network traffic but is better suited for accelerating TCP and UDP traffic; CloudFront is generally preferred for HTTP content delivery.

Option C migrates workloads to Lambda and Aurora global databases, which is an advanced and potentially costly redesign that may not be necessary. Option D suggests moving to ECS and multi-AZ RDS but does not address global content delivery efficiently.

Therefore, option A uses proven scalability and caching best practices aligned with AWS Well-Architected Framework pillars for performance and operational excellence.

A. Mutual TLS:Provides secure communication but does not integrate with AWS credential exchange.

B. AWS Signature v4:Requires direct integration with AWS and is less secure for external systems.

C. Kerberos:Not natively supported for AWS API authentication.

D. IAM Roles Anywhere:Enables AWS API access using X.509 certificates without long-term credentials.

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

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.

The Requester Pays feature in Amazon S3 allows the bucket owner to configure the bucket so that the requester, rather than the bucket owner, pays for data transfer and request costs. This is ideal for sharing large datasets with the public or with other AWS accounts when you want to minimize your own data transfer expenses.

Reference Extract from AWS Documentation / Study Guide:

"Requester Pays buckets allow you to configure the bucket so that the requester instead of the bucket owner pays the cost of the request and the data download from the bucket."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Cost Management section.

To upload photos to an S3 bucket using Amazon CloudFront with a custom domain name, the following components are required:

ACM in us-east-1 (Option A): When using CloudFront with HTTPS, the SSL/TLS certificate must be created in theus-east-1Region. AWS Certificate Manager (ACM) handles the provisioning, management, and renewal of public certificates, making this a cost-effective and low-maintenance solution.

S3 Transfer Acceleration (Option C): Transfer Acceleration allows faster uploads to S3 from CloudFront by routing traffic through AWS’s edge locations. This significantly speeds up the data upload process, especially for users that are geographically distant from the S3 bucket's region.

Option B (ACM in eu-west-1): CloudFront only supports certificates created in us-east-1.

Option D and E (OAC and website endpoint): These are not ideal for handling secure uploads or efficient data transfers in this case.

AWS References:

Using ACM with CloudFront

Amazon S3 Transfer Acceleration

Storage Gateway Volume Gateway (Cached Volumes): This configuration allows you to store your primary data in Amazon S3 while retaining frequently accessed data locally in a cache for low-latency access.

Low-Latency Access: Frequently accessed data is cached locally on-premises, providing low-latency access while the less frequently accessed data is stored cost-effectively in Amazon S3.

Implementation:

Deploy a Storage Gateway appliance on-premises or in a virtual environment.

Configure it as a volume gateway with cached volumes.

Create volumes and configure your applications to use these volumes.

Minimal Infrastructure Changes: This solution integrates seamlessly with existing on-premises infrastructure, requiring minimal changes and reducing dependency on on-premises storage servers.

EC2 Account Attributes: Amazon EC2 allows you to set account attributes to automatically encrypt new EBS volumes. This ensures that all new volumes created in your account are encrypted by default.

Configuration Steps:

Go to the EC2 Dashboard.

Select "Account Attributes" and then "EBS encryption".

Enable default EBS encryption and select the default AWS KMS key or a customer-managed key.

Prevention of Unencrypted Volumes: By setting this account attribute, you ensure that it is not possible to create unencrypted EBS volumes, thereby enforcing compliance with security requirements.

Operational Efficiency: This solution requires minimal configuration changes and provides automatic enforcement of encryption policies, reducing operational overhead.

Amazon QLDB is the most cost-effective and suitable service for maintainingimmutable, cryptographically verifiable logsof data changes. QLDB provides a fully managed ledgerdatabase with a built-in cryptographic hash chain, making it ideal for recording changes to accounting records, ensuring data integrity and security.

QLDB reduces operational overhead by offering fully managed services, so there’s no need for server management, and it’s built specifically to ensure immutability and verifiability, making it the best fit for the given requirements.

Option A (Redshift): Redshift is designed for analytics and not for immutable, cryptographically verifiable logs.

Option B (Neptune): Neptune is a graph database, which is not suitable for this use case.

Option C (Timestream): Timestream is a time series database optimized for time-stamped data, but it does not provide immutable or cryptographically verifiable logs.

AWS References:

Amazon QLDB

How QLDB Works

Amazon RDS for Oracle is a managed database service, which significantly reduces operational overhead compared to running Oracle on EC2 or on-premises. AWS Secrets Manager natively integrates with RDS and supports automatic, scheduled password rotation with minimal setup. You can configure the rotation schedule (including yearly), and Secrets Manager will handle the secure password storage and rotation workflow for you.

AWS Documentation Extract:

"AWS Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own infrastructure. You can configure automatic rotation for supported databases such as Amazon RDS for Oracle."

(Source: AWS Secrets Manager documentation)

A, C, D: These solutions require custom scripting, Lambda, and alarms, leading to more operational overhead.

DynamoDB Streams: Captures real-time changes in DynamoDB tables and allows integration with Lambda for processing the changes.

Minimal Operational Overhead: Using a Lambda function directly to write data to S3 ensures simplicity and reduces the complexity of the pipeline.

Amazon DynamoDB Streams Documentation

AWSService Catalogis designed to allow organizations to manage a catalog of approved products (including AMIs) that users can deploy. By creating a portfolio that contains only EC2 instances launched with preapproved AMIs, the company can enforce compliance with the approved operating system and software for all EC2 instances. Service Catalog also streamlines the process of launching EC2 instances, reducing the lead time while ensuring that developers use only the approved configurations.

Option B (EC2 Image Builder): While EC2 Image Builder helps in creating and managing AMIs, it doesn't provide the enforcement mechanism that Service Catalog does.

Option C (EventBridge rule and Systems Manager script): This solution is reactive and involves more operational complexity compared to Service Catalog.

Option D (AWS Config rule): This option is reactive (it terminates non-compliant instances after launch) and introduces additional operational overhead.

AWS References:

AWS Service Catalog

Amazon SQS with a 2-day retention ensures the data lives just as long as needed. EventBridge Pipes allow direct integration between event producers and consumers, with optional filtering and transformation. AWS Step Functions supports manual approval steps, which fits the workflow requirement perfectly.

The best practice for secure access control in AWS is to use IAM roles with least-privilege policies, granting only the permissions necessary to perform required tasks. Assigning roles individually ensures that developers cannot overstep their intended access boundaries.

Sharing credentials or using permanent access keys increases the risk of security breaches. Cognito is primarily intended for managing user access to applications, not AWS infrastructure. Thus, Option B best meets security and access control requirements.

Analysis:

The solution must handle variable sales volumes, preserve transaction information, and store data in an Amazon Aurora database with minimal operational overhead. UsingAPI Gateway, AWS Lambda, and SQSis the best option because it provides scalability, reliability, and resilience.

Why Option A is Correct:

API Gateway: Serves as an entry point for transaction data in a serverless, scalable manner.

AWS Lambda: Processes the transactions and sends them to Amazon SQS for queuing.

Amazon SQS: Buffers the transaction data, ensuring durability and resilience against spikes in transaction volume.

Second Lambda Function: Processes messages from the SQS queue and updates the Aurora database, decoupling the workflow for better scalability.

Dead-Letter Queue (DLQ): Ensures failed transactions are logged for later debugging or reprocessing.

Why Other Options Are Not Ideal:

Option B:

Using an ALB with ECS on EC2 introduces operational overhead, such as managing EC2 instances and scaling ECS tasks.Not cost-effective.

Option C:

EKS is highly operationally intensive and requires Kubernetes cluster management, which is unnecessary for this use case.Too complex.

Option D:

Amazon Data Firehose and DMS are not designed for real-time transactional workflows. They are better suited for data analytics pipelines.Not suitable.

AWS References:

Amazon API Gateway:AWS Documentation - API Gateway

AWS Lambda:AWS Documentation - Lambda

Amazon SQS:AWS Documentation - SQS

Amazon Aurora:AWS Documentation - Aurora

Aurora PostgreSQL supports the pgvector extension, which allows storage and querying of vector embeddings directly inside the database. This eliminates the need for external vector databases and provides cost-effective and performant integration for AI workloads.

This solution directly addresses the scalability and high availability requirements with minimal downtime.

Additional Reader Instances: Adding more reader instances to the Aurora cluster will distribute the read load, improving the performance of the application under heavy read traffic. Aurora reader instances automatically replicate the data from the writer instance, enabling you to scale out read operations.

Amazon RDS Proxy: RDS Proxy improves database availability by managing database connections more efficiently and providing a connection pool. This reduces the overhead on the Aurora cluster during peak loads, further enhancing performance and availability without requiring changes to the application code.

Why Not Other Options?:

Option A (EventBridge and Lambda): This doesn’t directly address the performance and availability issues. Logging state changes and adding reader nodes on failure events doesn’t provide proactive scalability.

Option B (Zero-Downtime Restart and Activity Streams): Zero-Downtime Restart (ZDR) is useful for minimizing downtime during maintenance but doesn’t directly improve scalability. Database Activity Streams are more for security monitoring than for performance enhancement.

Option D (ElastiCache for Redis): While adding a caching layer can help with read performance, it introduces complexity and may not be necessary if additional reader instances can handle the load.

AWS References:

Amazon Aurora Scaling- Information on scaling Aurora clusters with reader instances.

Amazon RDS Proxy- Details on how RDS Proxy can improve database performance and availability.

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 Data Lifecycle Manager (DLM) automates the creation, retention, and deletion of EBSsnapshots. This allows organizations to define policies that ensure snapshots are only kept as long as needed, reducing costs automatically and minimizing manual effort. AWS recommends using DLM for optimizing storage and managing backup lifecycle with minimal overhead.

DAX does not support enabling encryption at rest on an existing cluster. To use encryption at rest, you must create a new DAX cluster with encryption enabled at creation time and migrate workloads accordingly.

Why Option D is Correct:

IAM Roles with Instance Profiles: Allow applications to access AWS services securely without hardcoding long-term access keys.

Short-Term Credentials: IAM roles issue short-term credentials dynamically managed by AWS.

Why Other Options Are Not Ideal:

Option A and B: Embedding or retrieving long-term access keys introduces security risks and operational overhead.

Option C: Combining IAM roles with Parameter Store adds unnecessary complexity.

AWS References:

IAM Roles and Instance Profiles:AWS Documentation - IAM Roles

AWS Service Catalogallows organizations to centrally manage commonly deployed IT services and offers self-service deployment capabilities to customers. By creatingService Catalog products, the consulting company can package their solutions and tools for easy reuse by customers while maintaining central control over configuration and access. This provides a standardized and automated solution with the least operational overhead for managing and deploying solutions across different customers.

Option A (CloudFormation): CloudFormation templates are useful but don't provide the same level of management and user-friendly self-service capabilities as Service Catalog.

Option C (Systems Manager): Systems Manager is more focused on managing infrastructure and doesn't offer the same self-service capabilities.

Option D (AWS Config): AWS Config is used for tracking resource configurations, not for deploying solutions.

AWS References:

AWS Service Catalog

Creating aVPC endpointfor S3 and configuring abucket policyto allow access only from the endpoint ensures that only EC2 instances within the VPC can access the S3 bucket. This solutionimproves security by restricting access at the network level without the need for public internet access.

Option A (IAM policies): IAM policies alone cannot restrict access based on the network location.

Option B and D (Encryption): Encryption secures data at rest but does not restrict network access to the bucket.

AWS References:

Amazon S3 VPC Endpoints

Comprehensive and Detailed Explanation:

To optimize storage costs, migrating data from Amazon EFS to Amazon S3 with the Intelligent-Tiering storage class is a cost-effective solution.

Amazon S3 Intelligent-Tiering: This storage class automatically moves data between frequent, infrequent, and archive access tiers based on access patterns, reducing storage costs without impacting performance.

Cost Savings: By leveraging Intelligent-Tiering, the company can achieve significant cost savings, especially for data with unpredictable access patterns.

Application Update: The application must be updated to interact with Amazon S3 APIs for storing and retrieving files, ensuring seamless integration with the new storage solution.

This approach provides a scalable, durable, and cost-effective storage solution, aligning with the company's optimization goals.

AWS Glue jobs are designed for extract, transform, and load (ETL) operations, which are perfect for transforming clinical trial data. AWS Glue integrates with AWS Key Management Service (KMS), allowing for customer-specific encryption keys, fulfilling the encryption requirement with minimal operational effort. Client-side encryption with AWS KMS ensures that the data is encrypted before it is sent to S3, aligning with the security needs specified in the scenario.

Key aspects:

AWS Glue: This managed ETL service simplifies data transformation, reduces operational overhead, and integrates seamlessly with KMS.

CSE-KMS: Client-side encryption with KMS ensures that the data is encrypted with customer-specific keys before it is processed or stored in S3, offering robust security​​.

Minimal Operational Overhead: Compared to managing an EMR cluster, AWS Glue automates much of the process, making it a lower-effort solution.

AWS Documentation: According to the AWS Well-Architected Framework, encryption with AWS KMS offers strong security controls that meet the needs of industries requiring high levels of confidentiality​​.

Amazon CloudWatch Container Insightsis designed for monitoring containerized environments like EKS. It provides native support for collecting and visualizing metrics and logs in a centralized location through CloudWatch dashboards, offering the most operationally efficient solution.

Option A:Using the CloudWatch agent provides basic metrics but lacks the specific insights required for containerized applications.

Option B:Kinesis Data Streams and Firehose add unnecessary complexity for this use case.

Option C:CloudTrail is for auditing API activity and is not designed for application metrics and log aggregation.

AWS Documentation References:

Amazon CloudWatch Container Insights

A warm pool is an Auto Scaling feature that keeps instances in a pre-initialized state so they can quickly join the active group when scaling is required. This reduces the time needed for instance bootstrapping and makes new capacity available almost instantly. Option A only increases capacity limits but does not address slow boot times. Option C merely extends grace periods without solving the delay. Option D forces overprovisioning, which is wasteful and not aligned with cost optimization. Using a warm pool (B) directly addresses the problem by reducing response time to scaling events.

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

Amazon FSx for ONTAP supports both NFS and SMB protocols and includes automated tiering between SSD and capacity pool storage, optimizing cost and performance. It is ideal for mixed operating systems and varied access patterns with minimal administrative overhead.

Amazon S3 Intelligent-Tiering: This storage class is designed to optimize costs by automatically moving data between two access tiers (frequent and infrequent) when access patterns change. It provides cost savings without performance impact or operational overhead.

S3 Lifecycle Rules: By creating an S3 Lifecycle rule, the company can automatically transition objects to the Intelligent-Tiering storage class. This eliminates the need for manual intervention and ensures that objects are moved to the most cost-effective storage tier based on their access patterns.

Operational Efficiency: Intelligent-Tiering requires no additional management and delivers immediate availability for frequently accessed objects. This makes it the most operationally efficient solution for the given requirements.

When on-premises DNS servers need to resolve private DNS names in a VPC, the correct pattern is to create a Route 53 Resolver inbound endpoint. The inbound endpoint allows DNS queries to flow from the on-premises environment into the VPC, where Route 53 can resolve VPC-specific names (such as private hosted zones or private resource records). Outbound endpoints (C) are for sending VPC DNS queries to on-premises, not the reverse. Verified Access (A) is unrelated to DNS resolution. Direct Connect (B) provides network connectivity but does not provide DNS forwarding capabilities. Therefore, option D is the correct design.

Amazon EBS io2 Block Express volumes are designed to deliver sub-millisecond latency and up to 256,000 IOPS per volume, with durability and high availability. This makes io2 Block Express the recommended choice for workloads requiring very high and predictable IOPS, such as enterprise databases and high-transaction-rate applications.

Reference Extract:

"EBS io2 Block Express volumes deliver up to 256,000 IOPS and sub-millisecond latency, supporting high-performance, high-durability workloads."

Source: AWS Certified Solutions Architect – Official Study Guide, EBS Performance 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

A Network Load Balancer (NLB) supports IP address-based targets, which allows the use of both EC2 and on-premises endpoints. It also supports a static IP address or Elastic IP, which meets the requirement for a fixed IP address needed by some users.

NLB offers high performance, low latency, and minimal operational overhead. Application Load Balancer only supports instance or Lambda targets, not IP addresses outside the VPC. Route 53 Resolver is for DNS, and API Gateway is for HTTP-based APIs, not web applications.

AWS Lambda supports encrypting environment variables at rest using AWS KMS. You can use encryption helpers (or Lambda’s built-in support) to encrypt sensitive environment variable values using a KMS key. These encrypted variables are not visible in plaintext to developers, either in the console or when running the code.

AWS Documentation Extract:

"AWS Lambda automatically encrypts environment variables at rest. For additional security, you can use AWS KMS keys and encryption helpers to encrypt environment variables, ensuring they are never exposed in plaintext."

(Source: AWS Lambda documentation, Environment Variables Security)

A: Does not address the issue (and adds more management overhead).

B, C: There is no native support for environment variable encryption via CloudHSM or ACM.

Comprehensive and Detailed Explanation:

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data. By using Amazon ECS with the AWS Fargate launch type, you can run containers without managing servers or clusters. This combination reduces operational overhead and provides scalability.

To handle temporary high workloads, Amazon RDS provisioned IOPS can be increased to meet the expected spike in database throughput.

Amazon EFS Elastic throughput mode is ideal for workloads with unpredictable or spiky traffic patterns. It automatically scales throughput based on the file system's size and activity, which is more effective than Bursting throughput during high sustained activity like a marketing campaign.

Multi-AZ improves availability but does not directly address performance. Migrating to PostgreSQL during a short campaign period introduces unnecessary complexity.

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

The requirements include:

Scalability: The solution must scale as video files are uploaded.

Long-running tasks: Processing tasks can take up to 30 minutes. AWS Lambda has a maximum execution time of 15 minutes, which rules out options that involve Lambda performing all the processing.

Serverless and event-driven architecture: Ensures cost-effectiveness and high availability.

Analysis of Options:

Option A:

AWS Lambda has a 15-minute timeout, which cannot support tasks that take up to 30 minutes.

EventBridge Scheduler is unnecessary for monitoring files when native event notifications are available.Not a valid choice.

Option B:

AWS Step Functions and AWS Fargate can handle long-running processes, but Amazon EFS is not the ideal storage for uploaded video files in a serverless architecture.

Processing tasks triggered by EFS events are not a common pattern and may introduce complexities.Not the best practice.

Option C:

Amazon S3 is used for storing uploaded files, which integrates natively with event-driven services like EventBridge and Step Functions.

Amazon S3 event notifications trigger a Step Functions workflow, which can orchestrate Fargate tasks to process large video files, meeting the scalability and execution time requirements.Correct choice.

Option D:

Similar to Option A, AWS Lambda cannot handle long-running processes due to its 15-minute timeout.

Invoking Lambda for processing directly is not feasible for tasks that take up to 30 minutes.Not a valid choice.

AWS References:

Amazon S3 Event Notifications:AWS Documentation - S3 Event Notifications

AWS Step Functions:AWS Documentation - Step Functions

AWS Fargate:AWS Documentation - Fargate

Comparison of Storage Services:AWS Storage Options

By leveragingAmazon S3,Step Functions, andFargate, this solution provides a scalable, efficient, and serverless approach to handling video processing tasks.

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.

The best practice for granting AWS service access to EC2 instances is to assign IAM roles with the least privilege necessary. IAM roles attached to EC2 instances provide temporary credentials automatically rotated by AWS, avoiding hard-coded credentials in the application code.

Option C adheres to security best practices by assigning an IAM role scoped with the minimum permissions needed to access the S3 bucket.

Option A grants administrative access, which violates least privilege principles. Options B and D use IAM users with static credentials, increasing the risk of credential exposure and requiring manual rotation, which is not recommended.

Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data in AWS. To scale across Regions and accounts in AWS Organizations, Macie supports delegated administration, automated sensitive data discovery, and multi-account aggregation through a centralized admin account.

The requirement is an event-driven pub/sub system that guarantees ordered delivery of events.

Amazon SNS FIFO topics provide the publish-subscribe model along with FIFO (First-In-First-Out) delivery and exactly-once message processing, ensuring ordered delivery to multiple subscribers.

Option A, EventBridge, provides event buses but does not guarantee event ordering across multiple subscribers. Option C (SNS standard topics) provides pub/sub but without ordering guarantees. Option D (SQS FIFO queues) guarantees order but are point-to-point queues, not pub/sub.

Thus, Amazon SNS FIFO topics meet the requirements for ordered pub/sub messaging.

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.

Amazon RDS Multi-AZ deployments provide automatic failover for relational databases such as MySQL, ensuring high availability and durability. The feature maintains synchronous replication between a primary DB instance and a standby in a separate Availability Zone. AWS guarantees that failover typically completes within minutes, ensuring an RTO and RPO of less than 2 minutes. Option A requires manual promotion of replicas, which cannot meet the strict RTO/RPO requirement. Option C depends on custom scripts and manual synchronization, introducing operational risk. Option D creates active-active EC2-based databases, which do not provide synchronous replication or automated failover. Therefore, Multi-AZ RDS (B) is the managed, resilient, and operationally efficient solution that meets the business requirements.

Detailed Explanation:

A. EventBridge Rule:Detects modifications to CloudFront distributions in real time and triggers the Lambda function for further action.

B. ALB + Config:Focuses on ALB security violations, not relevant for CloudFront logging changes.

C. Lambda + SNS:Provides real-time notifications about changes in logging configuration.

D. GuardDuty:Focuses on threat detection, not logging configuration changes.

E. API Gateway + WAF:Unrelated to CloudFront logging changes.

Option B:A gateway endpoint for S3 allows traffic to S3 without using public IPs and integrates with route tables.

Option D:Deploying EC2 instances in a private subnet with a NAT gateway enables outbound internet connectivity for other requirements without public IPs.

Option A:Egress-only internet gateways are for IPv6 traffic and do not work for IPv4 in this context.

Option C:Interface endpoints are not required for S3 as gateway endpoints are more suitable and cost-effective.

Option E:A customer gateway is for hybrid connectivity (e.g., on-premises), not suitable for this case.

AWS Documentation References:

VPC Endpoints

Amazon S3 Gateway Endpoints

A. VPC peering:Creates a fully meshed architecture, which is complex to manage for multiple VPCs.

B. Transit gateway:Simplifies network management by connecting multiple VPCs and on-premises networks via a central hub.

C. PrivateLink:Restricts communication to the application endpoint but may not allow full VPC connectivity.

D. ALB with internet exposure:Not secure or specific to private network communication.

Detailed Explanation:

A. IAM identity provider:Does not support centralized management across multiple accounts.

B. AWS Managed AD:Unnecessary if an on-premises Active Directory already exists.

C. IAM Identity Center + Existing AD:Best approach to integrate existing Active Directory for SSO, with MFA and centralized permissions.

D. Lambda for synchronization:Adds complexity and does not leverage IAM Identity Center capabilities.

Amazon EventBridge supports routing application-generated events to AWS Lambda targets. The Lambda function can process and insert events into a DynamoDB table. This is a standard design pattern for connecting event-driven applications with DynamoDB. Option A confuses DynamoDB Streams (which streams changes from DynamoDB) with EventBridge (which is for event routing). Options C and D reference partner event buses, which are intended for SaaS integrations, not custom application events. Therefore, the correct and simplest solution is to use a custom EventBridge event bus with a Lambda target (B).

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

Amazon S3 uses gateway VPC endpoints, which enable private, secure access to S3 without traversing the internet, compatible with IPv6.

Amazon DynamoDB uses interface VPC endpoints (powered by AWS PrivateLink) for private connectivity within the VPC.

Therefore, for secure private communication without public internet, the correct solution is to implement a gateway VPC endpoint for S3 and an interface VPC endpoint for DynamoDB.

Option B is incorrect because S3 does not support interface endpoints; Option C is incorrect because DynamoDB does not support gateway endpoints. Option D reverses the correct endpoint types.

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

Amazon Kinesis allows real-time ingestion of game events at scale, while Amazon DynamoDB provides millisecond-latency access to user data, automatically scaling with demand. This combination ensures real-time processing and fast data retrieval without managing infrastructure.

SSE-KMS: Server-side encryption with AWS Key Management Service (SSE-KMS) provides robust encryption of data at rest, integrated with AWS KMS for key management and auditing.

Automatic Key Rotation: By enabling automatic rotation for the KMS keys, the system ensures that keys are rotated annually without manual intervention, meeting compliance requirements.

Logging and Auditing: AWS KMS automatically logs all key usage and management actions in AWS CloudTrail, providing the necessary audit logs.

Implementation:

Create a KMS key with automatic rotation enabled.

Configure the S3 bucket to use SSE-KMS with the created KMS key.

Ensure CloudTrail is enabled for logging KMS key usage.

Operational Efficiency: This solution provides encryption, automatic key management, and auditing in a seamless, fully managed way, reducing operational overhead.

Amazon S3 is the most scalable and cost-optimized object storage for large amounts of data. It integrates natively with Amazon SageMaker, which allows direct access to S3 data for machine learning training jobs without the need to copy data elsewhere. The S3 Intelligent-Tiering storage class further optimizes storage costs for data with unknown or changing access patterns, while maintaining immediate availability for ML training.

Copying to FSx for Lustre or EFS adds unnecessary complexity and cost unless ultra-high throughput POSIX file access is specifically required, which is not mentioned here.

AWS Documentation Extract:

"Amazon S3 provides cost-effective storage for ML data sets, and Amazon SageMaker can directly access data in S3. S3 Intelligent-Tiering automatically moves data between frequent and infrequent access tiers, optimizing costs for changing data access patterns."

(Source: AWS S3 and SageMaker documentation)

A, D: FSx for Lustre or EFS may be used in specialized cases but are not cost-optimized for general large-scale ML data storage.

C: SageMaker notebook instance storage is not designed for large-scale data ingestion or long-term storage.

A: Amazon Macie can scan S3 buckets for sensitive data and identify PII.

C: S3 Object Lock legal hold prevents object deletion until a review is complete, meeting compliance requirements.

E: EventBridge can trigger the Lambda function automatically when Macie detects sensitive data, creating an automated workflow.

AWS Documentation Extract:

"Amazon Macie automatically discovers, classifies, and protects sensitive data in AWS. You can use EventBridge to invoke a Lambda function for post-processing, such as applying Object Lock legal hold to flagged objects."

(Source: AWS Macie and S3 Object Lock documentation)

B, D: Moving to Glacier Deep Archive or applying retention in governance mode is not specific to deletion prevention pending review.

F: MFA Delete adds a security layer but does not automate object retention for flagged PII.

TheGateway Load Balancer (GWLB)is specifically designed to route traffic through a security appliance in a hub-and-spoke model, making it the ideal solution for inspecting traffic between multiple AWS accounts. GWLB enables you to simplify, scale, and deploy third-party virtual appliances transparently, and it can work across multiple VPCs or accounts using interface endpoints (Gateway Load Balancer Endpoints).

Key AWS features:

Traffic Inspection: The GWLB allows the centralized security appliance to inspect traffic between different VPCs, making it suitable for inspecting inter-account interactions.

Interface VPC Endpoints: By using interface endpoints in the application accounts, traffic can securely and efficiently be routed to the security appliance in the networking account.

AWS Documentation: The use of GWLB aligns with AWS’s best practices for centralized network security, simplifying architecture and reducing operational complexity​.

Amazon Rekognition provides pretrained models that can detect inappropriate or unsafe content (such as nudity or violence) without requiring users to build their own ML models. Using a Lambda function to call Rekognition when new photos are uploaded is a serverless and scalable solution.

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.

The Amazon CloudWatch agent is required to collect memory utilization metrics (as memory metrics are not reported by default). A target tracking policy is the simplest and most effective way to scale based on a custom metric such as mem_used_percent.

Reference Extract:

"Install the CloudWatch agent to collect memory metrics, and create a target tracking scaling policy using these custom metrics."

Source: AWS Certified Solutions Architect – Official Study Guide, Monitoring and Scaling section.

Amazon DynamoDB supports both TTL (Time to Live) for automatic deletion and Point-in-Time Recovery (PITR) for backup and restore of table data. It's ideal for frequently updated data with short retention requirements and minimal operational overhead.

TTL attribute ensures expired items are automatically removed.

PITR enables recovery from accidental deletes or application errors.

Amazon EFS requires a mount target in each Availability Zone where EC2 instances access the file system. This is because each mount target provides an elastic network interface in the subnet and AZ, reducing network latency by allowing EC2 instances to communicate locally with the EFS mount target. Creating a mount target in each AZ optimizes file system access performance and availability. Instances mount the EFS file system via the mount target in their respective AZ, which provides the lowest possible latency and avoids cross-AZ traffic.

Option A, with only a single mount target in the VPC, will cause cross-AZ traffic for instances in other AZs, increasing latency and potentially incurring data transfer costs. Option B is incomplete and introduces complexity with sharing directories across instances. Option C is invalid because mount targets are per AZ and per subnet, not per instance.

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​​.

AWS Step Functions supports long-running workflows and error retries, making it ideal for a job composed of many tasks. Integration with EventBridge allows automatic triggering from S3 events. This setup is resilient and supports up to 1-year execution duration.

Amazon S3 Intelligent-Tiering is the most cost-effective solution for this scenario, providing both high availability and durability while adjusting automatically to changing access patterns. It moves data across two access tiers: one optimized for frequent access and another for infrequent access, based on usage patterns. This tiering ensures that the company avoids paying for unused storage while also keeping frequently accessed data in a more accessible tier.

Key AWS references and benefits ofS3 Intelligent-Tiering:

High Durability and Availability: Amazon S3 offers 99.999999999% durability and 99.9% availability for objects stored, ensuring data is always protected.

Automatic Tiering: Data is automatically moved between tiers based on access patterns, making it ideal for workloads with unpredictable or variable access patterns.

No Retrieval Fees: Unlike S3 One Zone-IA or Glacier, there are no retrieval fees, making this more cost-effective in scenarios where access patterns vary over time.

AWS Documentation: According to the AWS Well-Architected Framework under theCost Optimization Pillar, S3 Intelligent-Tiering is recommended for storage when access patterns change over time, as it minimizes costs while maintaining availability​.

Amazon EFS provides a fully managed, highly available, and shared file system that can be mounted by instances across multiple Availability Zones. This ensures consistency of files between EC2 instances and avoids replication issues. EBS volumes, in contrast, are AZ-scoped and not designed for multi-instance sharing. Options A and B rely on custom replication or manual file handling, which increases operational overhead and risks inconsistencies. Option D does not solve the shared access and consistency requirement. By migrating storage to EFS, both EC2 instances will read and write to the same storage system, ensuring that users always access the latest files without 404 errors.

For cost-effectiveness and high availability in Amazon EMR workloads, the best approach is to configure atransient cluster(which runs for the duration of the job and then terminates) withOn-Demand Instancesfor the primary and core nodes, andSpot Instancesfor the task nodes. Here's why:

Primary and core nodes on On-Demand Instances: These nodes are critical because they manage the cluster and store data on HDFS. Running them on On-Demand Instances ensures stability and that no data is lost, as Spot Instances can be interrupted.

Task nodes on Spot Instances: Task nodes handle additional processing and can be used with Spot Instances to reduce costs. Spot Instances are much cheaper but can be interrupted, which is fine for non-critical tasks as the framework can handle retries.

Atransient clusteris more cost-effective than a long-running cluster for workloads that only run for 6 hours a day. Transient clusters automatically terminate after the workload completes, saving costs by not keeping the cluster running when it's not needed.

Option A: A long-running cluster may result in unnecessary costs when the cluster isn't being used.

Option C: Running core nodes on Spot Instances risks data loss if the Spot Instances are interrupted, violating the requirement for zero data loss.

Option D: Running both core and task nodes on Spot Instances is highly risky for data-critical workloads.

AWS References:

Amazon EMR Cluster Management

Using Spot Instances in EMR

AWS Firewall Manager integrates with AWS Organizations to centrally manage and apply security group policies, AWS WAF rules, and AWS Shield Advanced protections. It automates the propagation of rules across accounts and Regions and can also audit and remediate noncompliant configurations.

Amazon Athenais a serverless query service that allows you to run SQL queries directly on data stored in Amazon S3 without the need for a data warehouse. It is cost-effective because you only pay for the queries you run, and it can handleApache Parquetfiles efficiently. Additionally, Athena integrates with KMS, making it suitable for querying encrypted data.

Key AWS features:

Cost-Effective: Athena charges only for the data scanned by the queries, making it a more cost-effective solution compared to Redshift or EMR for occasional queries.

Direct S3 Querying: Athena supports querying data directly in S3, including Parquet files, without needing to move the data.

AWS Documentation: Athena's compatibility with encrypted Parquet files in S3 makes it the ideal choice for this scenario, reducing both cost and complexity​​.

When designing a microservice architecture where each microservice interacts with different AWS services, it's essential to follow the principle of least privilege. This means granting each microservice only the permissions it needs to perform its tasks, reducing the risk of unauthorized access or accidental actions.

The recommended approach is to create individualIAM roleswith policies that grant each microservice the specific permissions it requires. Then, these roles should be associated with the EC2 instances that run the corresponding microservice. By doing so, each EC2 instance will assume its specific IAM role, and permissions will be automatically managed by AWS.

IAM roles provide temporary credentials via the instance metadata service, eliminating the need to hard-code credentials in your application code, which enhances security.

AWS References:

IAM Roles for Amazon EC2explains how EC2 instances can use IAM roles to securely access AWS services without managing long-term credentials.

Best Practices for IAMincludes recommendations for implementing the least privilege principle and using IAM roles effectively.

Why the other options are incorrect:

A. Assign an IAM user to each microservice: This requires managing long-term credentials (access keys), which should be avoided. Storing keys in application code is insecure and creates a maintenance burden.

B. Create a single IAM role: This violates the principle of least privilege, as a single role with broad permissions across all services is less secure.

C. Use AWS Organizations: This approach adds unnecessary complexity. Managing permissions at the account level for each microservice is excessive for this use case and doesn't adhere to the principle of least privilege.

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.

Option B (Amazon FSx for Lustre): FSx for Lustre is optimized for high-performance file systems required by HPC workloads, scaling to millions of IOPS and supporting parallelized data access.

Option E (Cluster Placement Group with Auto Scaling): A cluster placement group ensures low-latency communication between EC2 instances, critical for HPC workloads. Amazon EMR simplifies running large-scale analytics jobs.

Amazon FSx for Lustre Documentation,AWS Placement Groups Documentation

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 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.

Comprehensive and Detailed Explanation:

Amazon FSx for Lustre is a high-performance file system optimized for fast processing of workloads such as machine learning, high-performance computing (HPC), and video processing. It integrates natively with Amazon S3, allowing you to:

Access S3 Data: FSx for Lustre can be linked to an S3 bucket, presenting S3 objects as files in the file system.

High Performance: It provides sub-millisecond latencies, high throughput, and millions of IOPS, which are ideal for ML workloads.Amazon Web Services, Inc.

Minimal Operational Overhead: Being a fully managed service, it reduces the complexity of setting up and managing high-performance file systems.

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.

Using Amazon CloudFront in front of an ALB in a single region is a cost-effective way to deliver content with low latency across the globe. CloudFront caches content closer to the users, reducing the load on backend servers and minimizing data transfer costs by serving cached content from edge locations.

Compared to Global Accelerator, CloudFront is significantly more cost-optimized for static and dynamic content delivery. Multi-region deployments increase infrastructure and transfer costs, which violates the optimization goal. Therefore, option A provides the best mix of performance and cost efficiency.

Maintaining two NAT gateways for production ensures high availability. Reducing to one NAT gateway in non-production environments lowers cost while maintaining necessary connectivity. This approach is recommended by AWS for cost optimization in non-critical environments.

Reference Extract:

"For environments that do not require high availability, you can reduce costs by using a single NAT gateway and updating route tables accordingly."

Source: AWS Certified Solutions Architect – Official Study Guide, Cost Optimization and NAT Gateway section.

Usingcross-account IAM rolesis the most secure and scalable way to share data between AWS accounts.

Atrust relationshipallows the analytics team's account to assume the role in the main account and access the DynamoDB table directly.

Ais feasible but involves data duplication and additional costs for storing the JSON files in S3.

B and Cviolate security best practices by allowing public access to sensitive data and sharing credentials, which is highly discouraged.

AWS Documentation Reference:

Cross-Account Access with Roles

Best Practices for Amazon DynamoDB Security

Concurrency Scaling allows Amazon Redshift to add transient capacity automatically to handle bursts in concurrent queries. This is ideal for scenarios with predictable surge periods, such as end-of-month reporting.

It improves performance without manual resizing or cluster modification and without affecting availability. Resizing requires manual intervention and potential downtime, and Redshift Spectrum is designed for querying data in S3, not for solving concurrency issues.

AWS Glue provides a serverless ETL solution requiring minimal development. Glue supports conversion to Parquet with managed jobs and integrates with S3 for output.

AWS Documentation References:

AWS Glue Overview

S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves objects between frequent and infrequent access tiers as needed, with no retrieval fees for accessing data in any tier, and no performance impact. Minimal management overhead is required because AWS manages all transitions automatically. This class is cost-optimized and meets all requirements listed.

AWS Documentation Extract:

"S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable."

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

Other options:

B: Storage class analysis only provides recommendations, not actual storage tiering.

C & D: S3 Standard-IA and S3 One Zone-IA both have retrieval fees and may impact retrieval time and data durability.

AWS Budgets allows you to set custom cost and usage budgets. When actual or forecasted usage exceeds the threshold, AWS Budgets can automatically send alerts to an Amazon SNS topic. You can use AWS Cost Explorer in parallel for visual tracking of spending. This solution requires no code and has minimal operational overhead.

AWS Secrets Manager is the recommended service for managing and automatically rotating database credentials. It integrates natively with Amazon RDS (including PostgreSQL), supports built-in rotation functionality, and requires minimal setup.

Secrets Manager also supports versioning and auditing, which enhances operational excellence and security. Parameter Store does not natively support credential rotation. AWS KMS manages key encryption—not application secrets—so it is not applicable here.