SAA-C03 AWS Certified Solutions Architect - Associate (SAA-C03) Questions and Answers

AWS Shield Advanced provides advanced DDoS protection for AWS workloads, including EC2, CloudFront, and RDS. When integrated with CloudFront, Shield Advanced offers comprehensive detection and mitigation against large and sophisticated DDoS attacks, along with 24x7 access to the AWS DDoS Response Team (DRT). AWS WAF provides application-level protection, but for complete DDoS mitigation, Shield Advanced is the recommended solution.

Reference Extract from AWS Documentation / Study Guide:

"AWS Shield Advanced provides expanded DDoS attack protection for applications running on AWS. It offers always-on detection and automatic inline mitigations that minimize application downtime and latency."

Source: AWS Certified Solutions Architect – Official Study Guide, Security and DDoS Protection section.

AWS App2Container is a command-line tool that helps containerize existing Java and .NET applications running on-premises or on virtual machines, with minimal refactoring. By using Amazon EKS, the company benefits from a managed Kubernetes service, which significantly reduces the operational overhead compared to managing Kubernetes on EC2.

Amazon RDS for SQL Server provides a fully managed SQL Server database engine with automated backups, patching, and high availability through Multi-AZ deployments. This eliminates the need for the company to manage database infrastructure and software manually.

Overall, option A provides the most streamlined and managed approach for both the application and database layers with the least operational effort.

To provide unique, secure URLs efficiently:

A: Create a wildcard custom domain in Route 53 as an alias for the API Gateway endpoint.

D: Request a wildcard certificate in ACM in the same Region as API Gateway (certificates must be in the same Region as the API).

F: Create a custom domain name in API Gateway and attach the certificate.

“You can configure a custom domain name for your API Gateway API. To use a wildcard certificate, request it from ACM in the same Region as your API.”

— API Gateway Custom Domain Names

This combination provides secure wildcard URLs without creating separate endpoints or hosted zones per user.

Comprehensive and Detailed Explanation From Exact Extract of Amazon Web Services (AWS) Architect documents:

To send data privately from on-premises to S3 buckets across multiple AWS accounts:

A: Using AWS Direct Connect with a private virtual interface (VIF) enables private connectivity from on-premises into the VPC.

E: After establishing the central VPC in a networking account, use VPC peering to allow access to S3 buckets across multiple AWS accounts.

“Direct Connect private virtual interface (VIF) allows private IP connectivity to VPC resources.”

“VPC Peering enables routing of traffic between VPCs using private IP addresses.”

— AWS Direct Connect Documentation

— VPC Peering Guide

Why not others:

B: Public VIF sends traffic over public AWS services—not fully private.

C: Interface endpoint is useful within a single account/VPC context.

D: Gateway endpoint doesn't solve inter-account routing.

Explanation (AWS Docs):

DynamoDB has a built-in feature called Time to Live (TTL) which automatically deletes expired items without manual intervention. This requires adding a timestamp attribute and setting a TTL on the table. This is the lowest operational overhead approach.

“You can use DynamoDB TTL to automatically delete items after a specified time, reducing storage costs and administrative overhead.”

— DynamoDB TTL

AWS Secrets Manager is a fully managed service specifically designed to securely store and automatically rotate database credentials, API keys, and other secrets. Secrets Manager provides built-in integration with Amazon RDS for automatic credential rotation on a configurable schedule without requiring downtime. It also manages the secure distribution of the credentials to authorized services, such as your web servers, using IAM policies. Manual solutions (S3, files, cron jobs) do not provide the same level of automation, audit, or security.

Reference Extract from AWS Documentation / Study Guide:

"AWS Secrets Manager enables you to rotate, manage, and retrieve database credentials securely. It supports automatic rotation of secrets for supported AWS databases without requiring application downtime."

Source: AWS Certified Solutions Architect – Official Study Guide, Security and Secrets Management section.

Lambda supports mounting an Amazon EFS file system inside your function to store larger dependencies beyond the 250 MB layer quota.

EFS automatically encrypts data in transit using TLS.

“You can configure your Lambda function to mount an Amazon EFS file system, enabling your function to access large amounts of data or large dependencies.”

“Amazon EFS automatically encrypts all data at rest and in transit.”

— Lambda with Amazon EFS

This is the least operational overhead approach.

AWS Site-to-Site VPN is a cost-effective way to securely connect your on-premises data center with AWS resources. In this scenario:

Applications in private subnetsrequire access to the API hosted in the on-premises data center.

ASite-to-Site VPN connectionis a secure and cost-efficient option to route traffic between the VPC and on-premises resources.

Transit GatewayandPrivateLinkare not cost-effective for this use case.

NAT Gatewayonly provides internet access for private subnets, which is not suitable for reaching an on-premises resource.

AWS Documentation Reference:

AWS Site-to-Site VPN

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

Explanation (AWS Docs):

You cannot “place” DynamoDB inside a VPC. Instead, you use a VPC endpoint.

A Gateway VPC Endpoint for DynamoDB enables private connectivity between your VPC and DynamoDB without traversing the public internet.

“Use a gateway VPC endpoint to privately connect your VPC to DynamoDB without requiring an internet gateway or NAT.”

— Gateway VPC Endpoints

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.

Option Acombines ECS with Fargate for scalability, RDS for relational data, and SQS for decoupling with message ordering (FIFO queues).

Option Buses SNS, which does not maintain message order.

Option Cis suitable for serverless workflows but not relational data.

Option Drelies on Elastic Beanstalk, which offers less flexibility for scaling.

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.

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

AWS WAF (Web Application Firewall): AWS WAF allows you to create custom rules to block or allow web requests based on conditions that you specify.

Web ACL (Access Control List):

Create a web ACL and associate it with the ALB.

Use IP rule sets to specify the IP addresses of the retail locations that are allowed to access the application.

Security and Flexibility:

AWS WAF provides a scalable way to manage access control, ensuring that only traffic from registered IP addresses is allowed.

You can dynamically update the IP rule sets to add or remove IP addresses as needed.

Operational Simplicity: Using AWS WAF with a web ACL is straightforward and integrates seamlessly with the ALB, providing an efficient solution for managing access control based on IP addresses.

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.

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.

This solution is the most cost-effective and meets the RPO and RTO requirements of 24 hours.

Automatic Snapshots: Amazon RDS automatically creates snapshots of your DB instance at regular intervals. By copying these snapshots to another AWS Region every 24 hours, you ensure that you have a backup available in a different geographic location, providing disaster recovery capability.

RPO and RTO: Since the company’s RPO and RTO are both 24 hours, copying snapshots daily to another Region is sufficient. In the event of a disaster, you can restore the DB instance from the most recent snapshot in the target Region.

Why Not Other Options?:

Option A (Cross-Region Read Replica): This could provide a faster recovery time but is more costly due to the ongoing replication and resource usage in another Region.

Option B (DMS Cross-Region Replication): While effective for continuous replication, it introduces complexity and cost that isn’t necessary given the 24-hour RPO/RTO.

Option C (Cross-Region Native Backup Copy): This involves more manual steps and doesn’t offer as straightforward a solution as automated snapshot copying.

AWS References:

Amazon RDS Automated Backups and Snapshots- Details on automated backups and snapshots in RDS.

Copying an Amazon RDS DB Snapshot- How to copy DB snapshots to another Region.

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.

Comprehensive and Detailed Explanation:

Deploying the web application on EC2 instances in private subnets behind an internal ALB ensures that the application is not directly accessible from the internet. By setting up a Site-to-Site VPN connection, the application can be accessed securely from the company's office network. Deploying NAT gateways in public subnets allows instances in private subnets to initiate outbound connections to the internet for downloading security patches.

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

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

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

AWS References:

Amazon EFS Performance Modes

IAM Identity Center (formerly AWS SSO) is designed for:

Federated access from external directories (e.g., Active Directory, Okta)

Centralized permission management

Support for MFA

Granular control via Attribute-based access control (ABAC)

“IAM Identity Center allows you to manage SSO access to AWS accounts and business applications centrally. You can assign users and groups permissions based on directory attributes such as Region and job role.”

— IAM Identity Center Docs

This option ensures:

Federated, centralized access

Region-specific permissions

MFA and role mapping via existing directory service

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

Reference Extract from AWS Documentation / Study Guide:

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

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

Option Censures dynamic scaling based on demand using a target tracking scaling policy, optimizing costs.

Option Aresults in over-provisioning, leading to higher costs.

Option Bincreases costs by using larger instances.

Option Dis not feasible as instance store volumes are ephemeral and unsuitable for shared storage like EFS.

Amazon Aurora Serverless is a fully managed, MySQL-compatible database that automatically scales based on demand and provides high availability. Combining this with EC2 Auto Scaling and an Application Load Balancer achieves both application and database high availability and scalability.

Reference Extract:

"Aurora Serverless automatically starts up, shuts down, and scales capacity based on your application's needs, providing a cost-effective, highly available database solution."

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

AWS Transfer Family (SFTP) allows clients to use standard SFTP clients and SSH keys without changes. By enabling service-managed users, clients can continue uploading files with their existing tools.

The service delivers the files directly into S3, reducing latency between upload and processing. This removes the need for EC2, custom scripts, and periodic transfers. It fully meets the requirement for a managed solution with minimal disruption to client processes.

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 Direct Connect provides a dedicated network connection from on-premises to AWS, offering greater bandwidth and more consistent performance than internet-based connections or VPN. AWS PrivateLink enables secure, private connectivity to supported AWS services such as Kinesis Data Firehose over Direct Connect, bypassing the public internet and providing the highest throughput and lowest latency possible. This is the recommended solution for consistently transferring large volumes of data with maximum reliability and performance.

Reference Extract from AWS Documentation / Study Guide:

"AWS Direct Connect and AWS PrivateLink provide private, high-throughput connectivity between on-premises and AWS services, bypassing the public internet and ensuring maximum performance for large data transfers."

Source: AWS Certified Solutions Architect – Official Study Guide, Hybrid Networking section.

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

AWS Documentation Extract:

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

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

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

Other options:

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

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

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

Amazon RDS Proxy: RDS Proxy is a fully managed, highly available database proxy that makes applications more resilient to database failures by pooling and sharing connections, and it can automatically handle database failovers.

Reduced Failover Time: By using RDS Proxy, the connection management between the application and the database is improved, reducing failover times significantly. RDS Proxy maintains connections in a connection pool and reduces the time required to re-establish connections during a failover.

Configuration:

Create an RDS Proxy instance.

Configure the proxy to connect to the RDS for PostgreSQL DB instance.

Modify the application configuration to use the RDS Proxy endpoint instead of the direct database endpoint.

Operational Benefits: This solution provides high availability and reduces application timeouts during failovers with minimal changes to the application code.

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

Configuration:

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

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

Benefits:

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

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

Incremental Exports: Exporting DynamoDB data directly to Amazon S3 provides an automated, serverless way to make data available for analytics without operational overhead.

Analytics-Friendly Storage: Amazon S3 supports long-term analytics workloads and can integrate with tools like Athena or QuickSight.

DynamoDB Export to S3 Documentation

In this scenario, the application generates dynamic, single-use files that are distributed via CloudFront. Since the application source code cannot be changed, modifying the files at the origin or altering the generation process is not possible.

AWS CloudFront caching is designed to reduce data transfer costs by caching responses at edge locations closer to users. Enabling caching for the CloudFront distribution can store the files temporarily at the edge, minimizing repeated data transfers from the origin and thereby lowering costs. Although these files are single-use, CloudFront can be configured with appropriate cache-control headers or TTL (time-to-live) values to cache files for even short periods, which reduces data transfer.

Lambda@Edge (Option A) can compress files but requires modification of responses and adds operational complexity. Also, it may not be as cost-effective as caching, especially if files are large or numerous. Amazon S3 Transfer Acceleration (Option B) optimizes data transfer into S3 but doesn’t reduce transfer costs from CloudFront. Multipart uploads (Option D) are used to optimize large file uploads and do not impact cost related to distribution or caching.

AWS Backup is the most appropriate solution for managing backups with minimal operational overhead while meeting the regulatory requirement to retain data for 90 days and enabling point-in-time restore for up to 14 days.

AWS Backup: AWS Backup provides a centralized backup management solution that supports automated backup scheduling, retention management, and compliance reporting across AWS services, including Amazon RDS. By creating a backup plan, you can define a retention period (in this case, 90 days) and automate the backup process.

Point-in-Time Restore (PITR): Amazon RDS supports point-in-time restore for up to 35 days with automated backups. By using AWS Backup in conjunction with RDS, you ensure that your backup strategy meets the requirement for restoring data to a specific point in time within the last 14 days.

Why Not Other Options?:

Option A (RDS Automated Backups): While RDS automated backups support PITR, they do not directly support retention beyond 35 days without manual intervention.

Option B (Manual Snapshots): Manually creating and managing snapshots is operationally intensive and less automated compared to AWS Backup.

Option C (Aurora Clones): Aurora Clone is a feature specific to Amazon Aurora and is not applicable to Amazon RDS for Oracle.

AWS References:

AWS Backup- Overview of AWS Backup and its capabilities.

Amazon RDS Automated Backups- Information on how RDS automated backups work and their limitations.

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.

The most cost-effective, secure way for private subnets to access AWS services without public IP addresses is to use VPC endpoints:

Interface endpoints (powered by AWS PrivateLink) for services like S3, DynamoDB, etc.

Traffic stays on the AWS network.

“You can privately connect your VPC to supported AWS services using interface VPC endpoints powered by AWS PrivateLink. Instances in your VPC do not require public IP addresses.”

— VPC Endpoints Documentation

Why not others:

NAT gateways incur hourly + data transfer costs and use public IPs.

Internet gateways expose traffic to the internet.

Direct Connect is for private on-prem connectivity, not needed here.

Why Option A is Correct:

ElastiCache for Redis: Provides persistent, scalable caching for in-progress transactions and SQL queries. Redis supports data durability and advanced features, making it suitable for transactional workloads.

Integration with Aurora: Easily integrates with the Aurora cluster to improve query performance.

Why Other Options Are Not Ideal:

Option B: CloudFront and S3 are unsuitable for transactional caching. EC2 instance store volumes are ephemeral and lack persistence.

Option C: Memcached does not offer persistence or advanced transactional support, unlike Redis.

Option D: Combining Redis with EC2 instance store is unnecessary; Redis alone meets all caching requirements.

AWS References:

Amazon ElastiCache:AWS Documentation - ElastiCache

Amazon SQS FIFO queuesensure that orders are processed in the exact order received and maintain message deduplication.

AWS Lambdascales automatically, handling bursts and maintaining high availability in a cost-effective manner.

Option A and D:Amazon SNS does not guarantee ordered processing.

Option C:Standard SQS queues do not guarantee order.

AWS Documentation References:

Amazon SQS FIFO Queues

The requirement is for immediate processing of uploaded videos and prompt notification to users. According to AWS best practices for event-driven architectures, using S3 event notifications to trigger a Lambda function upon an object creation (upload) is the optimal solution for real-time processing. Lambda can process the file as soon as it is uploaded, ensuring low latency. Once processing is complete, Lambda can save the processed file back to S3 and use Amazon SNS to notify the user.

This approach uses managed services with minimal operational overhead, is scalable, and ensures event-driven processing with instant user feedback. AWS Amplify primarily facilitates application development and hosting but does not natively provide direct push notification support for this backend workflow; instead, SNS is designed for such notification scenarios.

Reference Extract from AWS Documentation / Study Guide:

"Amazon S3 can publish events to AWS Lambda when objects are created. AWS Lambda runs code in response to events and can interact with other AWS services. Amazon SNS is a flexible, fully managed pub/sub messaging and mobile notifications service for coordinating the delivery of messages to subscribing endpoints and clients."

Source: AWS Certified Solutions Architect – Official Study Guide, Event-driven Architectures section; AWS Lambda Developer Guide (S3 triggers); Amazon SNS User Guide.

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.

To handle unpredictable traffic surges and improve scalability, Auto Scaling is essential. Creating an AMI and deploying it into an Auto Scaling group behind an Application Load Balancer (ALB) allows AWS to automatically scale the number of EC2 instances based on demand.

This architecture improves availability and responsiveness by distributing traffic and launching instances when needed. Option A satisfies the need for high scalability and resilience. Other options either lack Auto Scaling or are more complex without added benefit in this context.

Step A:AWS WAF with managed rules protects the API against application-layer attacks, such as SQL injection and cross-site scripting (XSS).

Step C:Amazon Cognito provides secure authentication and supports federation with social IdPs using OIDC or SAML. It integrates seamlessly with API Gateway.

Option B:AWS Shield Advanced provides DDoS protection, which is not explicitly required in this scenario.

Option D:API keys provide identification, not authentication, and are insufficient for this use case.

Option E:IP filters in WAF are overly restrictive for federated authentication scenarios.

AWS Documentation References:

Amazon Cognito Federation

AWS WAF Managed Rules

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

Health Checks:

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

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

Load Distribution and Disaster Recovery:

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

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

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

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

Option Ais the best solution as it leveragesAWS Lambdafor serverless, scalable, and highly available processing and enrichment of clickstream data. Lambda can process the data in real-time, join it with the Aurora database data, and write the enriched results to Amazon S3. FromS3,Amazon Redshift Spectrumcan directly query the enriched data without needing to load the data into Redshift, enabling cost efficiency and high availability.

Why Other Options Are Incorrect:

Option B:EC2 Spot Instances are not guaranteed to be highly available, as Spot Instances can be interrupted at any time. This does not align with the requirement for high availability.

Option C:While ECS with AWS Fargate provides scalability, using EC2 for the COPY command introduces operational overhead and compromises high availability.

Option D:Kinesis Data Firehose and Athena are suitable for querying raw data, but they do not directly support enriching the data by joining with Aurora. This solution fails to meet the requirement for data enrichment.

Key AWS Features Used:

AWS Lambda:Real-time serverless processing with integration capabilities for Aurora and S3.

Amazon S3:Cost-effective storage for enriched data.

Amazon Redshift Spectrum:Direct querying of data stored in S3 without loading it into Redshift.

AWS Documentation References:

AWS Lambda Function Overview

Amazon Redshift Spectrum

Processing Streaming Data with Kinesis Data Streams

Amazon RDS Multi-AZ deployment provides automated failover and increased availability for MySQL databases. In case of infrastructure failure or power outage in one Availability Zone, RDS automatically fails over to a standby replica in another AZ, minimizing downtime. This is the AWS-recommended way to achieve high availability for relational databases.

AWS Documentation Extract:

"Amazon RDS Multi-AZ deployments provide high availability, failover support, and data redundancy for database instances. In the event of infrastructure failure, Amazon RDS performs an automatic failover to the standby."

(Source: Amazon RDS documentation, High Availability)

A: An ALB does not increase availability for a single database instance.

B: EC2 instance recovery does not move the instance across Availability Zones.

D: Termination protection prevents accidental deletion, not availability issues.

Key Requirements:

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

Optimize costs during low traffic periods.

Analysis of Options:

Option A:

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

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

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

Option B:

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

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

Option C:

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

Incorrect Approach:Lacks cost optimization.

Option D:

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

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

AWS Solution Architect References:

AWS Lambda Provisioned Concurrency

AWS Application Auto Scaling with Lambda

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.

Comprehensive and Detailed Explanation From Exact Extract of Amazon Web Services (AWS) Architect documents:

Amazon EFS Lifecycle Management enables automatic cost optimization by transitioning files that haven’t been accessed for a defined period (e.g., 7 days) from EFS Standard to EFS Infrequent Access (IA).

“Amazon EFS Lifecycle Management automatically moves files that haven’t been accessed for a set period to the EFS Infrequent Access storage class, reducing storage costs for infrequently accessed files.”

— Amazon EFS Documentation

Key Points:

EFS IA is ideal for files larger than 128 KB and accessed less frequently.

It’s seamless — no code or tools needed.

Meets requirement for cost optimization and high availability.

Incorrect Options:

A: File Gateway adds unnecessary complexity and does not use EFS.

B: Storing files locally breaks shared access and resiliency.

D: Glacier Deep Archive is cold storage — not "readily available."

For maximum resiliency and fault tolerance in a mission-critical scenario, AWS recommends redundant Direct Connect connections from multiple data centers to multiple AWS Direct Connect locations.

This protects against:

Data center failure

Device failure

Location outages

“For workloads that require high availability, we recommend that you use multiple Direct Connect connections at multiple Direct Connect locations.”

— AWS Direct Connect Resiliency Recommendations

Option C follows AWS maximum resiliency model.

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

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

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

Why Not Other Options?:

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

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

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

AWS References:

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

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

Amazon RDSBlue/Green Deploymentsis the ideal solution for upgrading the database version with minimal operational overhead and no data loss. Blue/Green Deployments allows you to create a separate, fully managed "green" environment with the upgraded database version. You can test the new version in the green environment while the "blue" environment continuesserving production traffic. Once testing is complete, you can seamlessly switch traffic to the green environment without downtime.

This solution provides:

Fast, non-disruptive upgrade: Traffic is only switched to the new environment after testing, ensuring zero data loss.

Minimal operational overhead: AWS handles the infrastructure management, reducing manual intervention.

Option A (Manual snapshot): This requires manual intervention and involves more operational overhead.

Option B (Native backup/restore): This approach is more labor-intensive and slower than Blue/Green Deployments.

Option C (DMS): AWS DMS adds unnecessary complexity for a simple version upgrade when Blue/Green Deployments can handle the task more efficiently.

AWS References:

Amazon RDS Blue/Green Deployments

Amazon Cognitoprovides scalable, serverless authentication, andLambda@Edgeis used for authorization, providing low-latency access control at the edge.Amazon CloudFrontserves the web application globally with reduced latency and ensures secure access for users around the world. This solution minimizes operational overhead while providing scalability and security.

Option B (Directory Service): Directory Service is more suitable for enterprise use cases involving Active Directory, not for web-based applications.

Option C (S3 Transfer Acceleration): S3 Transfer Acceleration helps with file transfers but does not provide authorization features.

Option D (Elastic Beanstalk): Elastic Beanstalk adds unnecessary overhead when CloudFront can handle global delivery efficiently.

AWS References:

Amazon Cognito

Lambda@Edge

A. Edge-optimized API + Caching:Reduces latency by using Amazon CloudFront for edge locations and enables caching for dynamic content. Compression reduces data transfer latency.

B. Regional API + Caching:Increases latency for global users due to the lack of edge locations.

C. Edge-optimized API + Reserved Concurrency:Reserved concurrency ensures Lambda availability but does not address latency for dynamic content.

D. Regional API + Reserved Concurrency:Lacks edge optimization, increasing latency for global users.

AWS Elastic Disaster Recovery (AWS DRS) enables fast, reliable, and cost-effective disaster recovery. It replicates on-premises machines to AWS using continuous block-level replication. It supports automated testing and failover, meeting aggressive RTO targets such as 15 minutes.

Amazon ECS with AWS Fargate provides serverless container compute that can scale tasks dynamically in response to demand. This matches the unpredictable scaling requirements of a gaming workload. Aurora Serverless v2 provides on-demand, autoscaling relational database capacity while supporting complex SQL queries. EC2 with Auto Scaling (A) works but requires significant management overhead. Lambda with DynamoDB (B) is not suitable because the workload requires a relational database and long-running processes. ParallelCluster with HPC (D) is designed for batch scientific workloads, not dynamic, interactive gaming. Option C provides the correct balance of elasticity, performance, and managed services for both compute and relational database needs.

S3 Object Lock: Enables Write Once Read Many (WORM) protection for data, preventing objects from being deleted or modified for a set retention period.

S3 Versioning: Helps maintain object versions and ensures a recovery path for accidental overwrites.

CloudFront Distribution: Ensures secure and efficient public access by serving content through an edge-optimized delivery network while protecting S3 data with OAC.

Bucket Policy for OAC: Restricts public access to only the CloudFront origin, ensuring maximum security.

Amazon S3 Object Lock Documentation

The combination of these solutions provides an efficient and automated way to migrate data while preserving metadata and ensuring cleanup:

Create a new S3 bucket with versioning enabled(Option A) to preserve object metadata like version IDs during migration.

UseS3 batch operations(Option B) to efficiently copy data from specific prefixes in the source bucket to the destination bucket, ensuring minimal overhead.

Use an S3 Lifecycle policy(Option F) to automatically delete the data from the source bucket after it has been migrated, reducing manual intervention.

Option C (CopyObject API): This approach would require more manual scripting and effort.

Option D (Same-Region Replication): SRR is designed for ongoing replication, not for one-time migrations.

Option E (DataSync): DataSync adds more complexity than necessary for this task.

AWS References:

S3 Batch Operations

S3 Lifecycle Policies

Amazon RDS for MySQL supports read replicas that offload read-intensive workloads such as reporting, leaving the primary instance free for write operations.

“You can use Amazon RDS read replicas to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database workloads.”

— Working with Read Replicas

This is the recommended approach for minimizing performance impact on the primary DB instance.

AWS Systems Manager Session Manager allows secure, auditable SSH-like access to EC2 instances without the need to open SSH ports or manage bastion hosts. For this to work in a private subnet, an interface VPC endpoint is required (not a gateway endpoint).

The EC2 instances must have the AmazonSSMManagedInstanceCore policy attached to their IAM roles to allow Systems Manager operations.

With Session Manager, all session activity can be logged centrally to Amazon CloudWatch Logs or S3, satisfying the audit requirement and improving operational efficiency over manual SSH and bastion configurations.

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

AWS Documentation Extract:

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

(Source: AWS GuardDuty documentation)

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

Amazon EFS offers an Infrequent Access (IA) storage class, which can be managed via EFS lifecycle policies. Frequently accessed files remain in the Standard storage class, while infrequently accessed files are automatically moved to the IA class, significantly reducing storage costs with minimal effort and no application changes.

Reference Extract:

"EFS lifecycle management automatically transitions files that are not accessed for a set period to the EFS Infrequent Access (IA) storage class, reducing storage costs."

Source: AWS Certified Solutions Architect – Official Study Guide, EFS and Lifecycle Management section.

Amazon S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves data between frequent and infrequent access tiers based on usage. This tier offers immediate access to all objects, regardless of which tier they are stored in, while optimizing storage costs. S3 Intelligent-Tiering also provides the same high durability, availability, and security as other S3 storage classes and supports access from external resources using standard S3 APIs. Lifecycle policies and Glacier classes are more suitable for archival when infrequent access is predictable, but retrieval from Glacier classes is not immediate and incurs extra charges and delays.

Reference Extract from AWS Documentation / Study Guide:

"S3 Intelligent-Tiering is designed to optimize costs by automatically moving data between two access tiers when access patterns change. Data is always available and immediately accessible, making it ideal for unknown or unpredictable access patterns."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Storage Classes section.

Amazon SQS FIFO queues guarantee the order of message delivery and ensure that each message is delivered exactly once. Paired with AWS Lambda, this creates a scalable, fault-tolerant architecture that processes each order in order and prevents duplicates, which is critical for ecommerce workflows.

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

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

To automatically identify sensitive data in Amazon S3 and monitor access patterns for suspicious activities:

Amazon Macie uses machine learning and pattern matching to discover and protect sensitive data in S3. It provides visibility into data security risks and enables automated protection against those risks.

Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior to protect AWS accounts and workloads. It analyzes events from AWS CloudTrail, VPC Flow Logs, and DNS logs.

By enabling both services, the company can automate the discovery of sensitive data and continuously monitor access patterns for potential security threats.

The company wants to reduce end-to-end load time for its global customer base.AWS Global Acceleratorprovides a network optimization service that reduces latency by routing traffic to the nearest AWS edge locations, improving the user experience for globally distributed customers.

AWS Global Accelerator:

Global Acceleratorimproves the performance of your applications by routing traffic through AWS’s global network infrastructure. This reduces the number of hops and latency compared to using the public internet.

By creating astandard acceleratorand configuring the existing NLBs as target endpoints, Global Accelerator ensures that traffic from users around the world is routed to the nearest AWS edge location and then through optimized paths to the NLBs in each region. This significantly improves end-to-end load time for global customers.

Why Not the Other Options?:

Option A (ALBs instead of NLBs): ALBs are designed for HTTP/HTTPS traffic and provide layer 7 features, but they wouldn’t solve the latency issue for a global customer base. The key problem here is latency, and Global Accelerator is specifically designed to address that.

Option B (Route 53 weighted routing): Route 53 can route traffic to different regions, but it doesn’t optimize network performance. It simply balances traffic between endpoints without improving latency.

Option C (Additional NLBs in more regions): This could potentially improve latency but would require setting up infrastructure in multiple regions. Global Accelerator is a simpler and more efficient solution that leverages AWS’s existing global network.

AWS References:

AWS Global Accelerator

By usingAWS Global Acceleratorwith the existing NLBs, the company can optimize global traffic routing and improve the customer experience by minimizing latency. Therefore,Option Dis the correct answer.

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

A. Fixed desired instances:Does not adapt to traffic load fluctuations, leading to inefficiencies.

B. Larger EC2 instances:Increases costs unnecessarily.

C. Target tracking scaling policy:Adjusts capacity based on actual demand, optimizing costs.

D. Instance store volumes:Not persistent and unsuitable for shared data across instances.

This architecture uses ALB for routing, Auto Scaling for elasticity, SQS to buffer unprocessed orders and decouple services, and RDS Multi-AZ for high availability and durability of transactional data. This ensures resilience and fault tolerance even during traffic spikes.

The question focuses on minimizing costs while serving static content to users in the US and Europe.

Option Auses a single S3 bucket and configures CloudFront to limit edge locations, reducing costs by using fewer edge locations while still improving performance.

Option Bmaximizes edge locations, which increases costs unnecessarily.

Options C and Dinvolve storing data in multiple regions, which increases storage and operational costs.Thus, Option A is the most cost-effective solution.

Comprehensive and Detailed Explanation From Exact Extract of Amazon Web Services (AWS) Architect documents:

EBS Elastic Volumes allow you to dynamically increase volume size, adjust performance, and change volume types without detaching the volume or restarting the instance, providing a cost-efficient and operationally simple solution.

"Amazon EBS Elastic Volumes allows you to easily adapt your volumes as your needs change. You can increase volume size, adjust performance, or change the volume type while the volume is in use."

— EBS Elastic Volumes Documentation

Why D is cost-effective:

You only pay for provisioned storage, and it can be adjusted on demand.

Lambda can be triggered to adjust the volume based on the user's input.

Incorrect Options:

A: S3 is object storage, not block storage.

B & C: EFS and FSx are network file systems—not appropriate for EBS replacement in EC2 block-level requirements and are more costly for block-type needs.

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.

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

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

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

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

Comprehensive and Detailed Explanation:

For applications requiring high network throughput and low latency between EC2 instances, AWS recommends using a Cluster Placement Group:

Cluster Placement Group: Packs instances close together inside an Availability Zone, enabling workloads to achieve the low-latency network performance necessary for tightly-coupled node-to-node communication.

Enhanced Networking: Selecting instance types that support enhanced networking provides higher bandwidth, higher packet-per-second (PPS) performance, and consistently lower inter-instance latencies.

By combining a Cluster Placement Group with enhanced networking-enabled instance types, the company can meet the stringent performance requirements of its real-time industrial application.

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.

To track costs by department, you must activate the custom department tag as a cost allocation tag in the AWS Organizations management account. Once activated, Cost Explorer and cost and usage reports can group costs by this tag for all linked accounts. The most operationally efficient way is to activate only the relevant department tag and create a cost and usage report grouped by that tag.

AWS Documentation Extract:

“To use a tag for cost allocation, you must activate it in the AWS Billing and Cost Management console. After activation, you can use the tag to group costs in Cost Explorer and reports.”

(Source: AWS Cost Management documentation)

A, E: aws:createdBy is not related to department cost grouping and is unnecessary.

B: Applying extra filters is optional; D is more direct and operationally efficient.

Amazon RDS Proxy is designed to manage a large number of database connections from applications, especially serverless applications that can scale quickly. It improves application availability and scalability by pooling and sharing established database connections. This reduces the overhead of database connections and prevents overload during traffic spikes.

AWS Control Tower: Provides a managed service to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of AWS Organizations and applies security controls (guardrails).

Networking Account:

Create a centralized networking account that includes a VPC with both private and public subnets.

This centralized VPC will manage and control the networking resources.

AWS Resource Access Manager (AWS RAM):

Use AWS RAM to share the subnets from the networking account with the other workload accounts.

This allows different workload accounts to utilize the shared networking resources without the need to manage their own VPCs.

Operational Efficiency: Using AWS Control Tower simplifies the setup and governance of multiple AWS accounts, while AWS RAM facilitates centralized management of networking resources, reducing operational overhead and ensuring consistent security and compliance.

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.

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

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

Security groups are stateful, but they cannot explicitly deny traffic — only allow.

Network ACLs are stateless and support explicit deny rules.To prevent Application A from sending traffic to Application B, configure a deny outbound rule in the network ACL of Application A’s subnet to block traffic to Application B’s subnet.

“Unlike security groups, network ACLs support both allow and deny rules, enabling you to explicitly block traffic.”

— Network ACLs

This is the correct method to block outbound traffic between subnets.

A. ElastiCache:Provides in-memory caching, not suitable for persistent, scalable databases.

B. DynamoDB Streams + Lambda:Manages replication manually, increasing latency and operational complexity.

C. Aurora Global Database:Provides high availability but does not support active-active configuration.

D. DynamoDB Global Tables:Provides active-active configuration and sub-second RPO.

Comprehensive and Detailed Explanation From Exact Extract of Amazon Web Services (AWS) Architect documents:

AWS Global Accelerator improves the performance and availability of applications by directing user traffic through the AWS global network of edge locations using anycast IP addresses. It reduces latency and jitter for global users accessing applications in a single Region.

Why this works:

Global Accelerator routes user requests to the nearest AWS edge location using AWS’s high-performance backbone network.

It then forwards traffic to the optimal endpoint — in this case, the public API hosted on EC2.

This is much more cost-effective and requires less operational complexity than deploying and maintaining multiple API Gateway endpoints across regions (Option B), or setting up Direct Connect links for every international location (Option A).

Option C requires no application change and is designed specifically for latency improvement and high availability.

???? References:

AWS Global Accelerator Documentation

Use Cases for Global Accelerator

Performance Improvements for Global Users

AmazonTextractcan extract text from the PDFs, andAmazon Comprehendis the most suitable service to analyze the extracted text for sentiment and insights. Comprehend offers a fully managed, low-operational overhead solution for analyzing text data. The results can then be stored in anAmazon S3bucket, ensuring scalability and easy access.

Option A: Athena is for querying structured data and is not suitable for sentiment analysis.

Option B: SageMaker adds complexity and is not necessary when Comprehend can handle sentiment analysis natively.

Option D: QuickSight is used for visualization and analytics, but it does not provide sentiment analysis.

AWS References:

Amazon Comprehend

Amazon Textract

This solution meets the requirement of encrypting each customer’s data separately with the least operational overhead by leveraging AWS Key Management Service (KMS).

Separate AWS KMS Keys: By creating separate KMS keys for each customer, you can ensure that each customer’s data is encrypted with a unique key. This approach satisfies the compliance requirement for separate encryption and provides fine-grained control over access to the keys.

Granular Access Control: AWS KMS allows you to define key policies and use IAM policies to grant specific permissions to the keys. This ensures that only authorized users or services can access the keys, thereby maintaining the principle of least privilege.

Logging and Monitoring: AWS KMS integrates with AWS CloudTrail, which logs all key usage and management activities. This provides an audit trail that is essential for meeting compliance requirements.

Why Not Other Options?:

Option A (Store keys in S3): Storing keys in S3 is not recommended because it does not provide the same level of security, access control, or integration with AWS services as KMS does.

Option B (Hardware security appliance): Deploying a hardware security appliance adds significant operational overhead and complexity, which is unnecessary given that KMS already provides a secure and centralized key management solution.

Option C (Single KMS key for all data): Using a single KMS key does not meet the requirement of encrypting each customer's data separately.

AWS References:

AWS Key Management Service (KMS)- Overview of KMS, its features, and best practices for key management.

Using AWS KMS for Multi-Tenant Applications- Guidance on how to design applications using KMS for multi-tenancy.

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

Amazon Aurora MySQL supports the SELECT INTO OUTFILE S3 SQL syntax to export query results directly to Amazon S3. This is an efficient and low-overhead method for archiving data.

Once data is in S3, Lifecycle rules can be configured to automatically transition older data to lower-cost storage classes (such as S3 Glacier) or delete it after a defined period, providing a cost-optimized and automated archive solution.

The Glue-based options involve more services and operational overhead. SCT is intended for database migrations, not for periodic data archival.

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

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

Other Options:

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

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

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

AWS References:

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

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

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

Crawling the Data:

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

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

Amazon Athena:

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

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

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

Comprehensive and Detailed Explanation From Exact Extract of Amazon Web Services (AWS) Architect documents:

This solution leverages:

Amazon Redshift Spectrum to query S3 data directly from Redshift.

Amazon Athena for ad-hoc analysis of S3 data.

Amazon QuickSight for unified visualization from multiple data sources.

“Redshift Spectrum enables you to run queries against exabytes of data in Amazon S3 without having to load or transform the data.”

“QuickSight supports both Amazon Redshift and Amazon Athena as data sources.”

— Redshift Spectrum

— Amazon QuickSight Supported Data Sources

This architecture allows scalable querying and visualization with minimum ETL overhead, ideal for BI dashboards.

Incorrect Options:

A: The query editor is not a BI tool.

B, D: Grafana is better for time-series data, not structured analytics or BI reports.

Amazon API Gateway supportsresource policies, which allow you to control access to your API by specifying the IP addresses or ranges that can access the API. By creating a resource policythat explicitly denies access to any IP address outside the allowed set, you can ensure that only trusted IP addresses (such as those from your internal network) can access the critical information in your API. This approach provides fine-grained access control without the need for additional infrastructure or complex configurations.

Option A (Private integration): API Gateway private integrations are for creating private APIs that are only accessible within a VPC, but this solution is about restricting access to certain IP addresses.

Option C (Private subnet and ACLs): Deploying the API in a private subnet and using network ACLs adds unnecessary complexity and isn't the best fit for HTTP APIs.

Option D (Security group): API Gateway doesn't have a security group because it isn't a resource inside a VPC. Instead, resource policies are the correct mechanism for controlling IP-based access.

AWS References:

Controlling Access to API Gateway with Resource Policies

Amazon S3 Bucket Keys reduce the cost of AWS KMS API requests by generating a data key at the bucket level instead of individually calling KMS for every object read or written. This approach is particularly effective when workloads, such as ML pipelines, involve reading large numbers of encrypted objects. Switching to AWS managed keys (A) does not reduce the frequency of API calls. Removing encryption (B) would violate compliance/security requirements. Using CloudHSM (C) adds cost and operational burden. Therefore, the correct solution is D — enabling S3 Bucket Keys with SSE-KMS, which significantly reduces decryption costs while maintaining secure encryption.

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

Reference Extract from AWS Documentation / Study Guide:

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

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

An Application Load Balancer supports path- and host-based routing, which makes it ideal for routing requests based on subdomains. EC2 Auto Scaling ensures that the number of instances adjusts dynamically based on traffic, which helps manage cost and performance during predictable peak hours.

AWS Config allows for creation of custom rules to monitor EBS configurations. Combined with CloudWatch metrics and Amazon SNS, custom rules can track over-provisioned IOPS and send alerts when thresholds are breached, allowing proactive cost and performance management.

Option Ais the most automated and cost-efficient solution for exporting data to S3 for analytics.

Option Binvolves manual setup of Streams to S3.

Options C and Dintroduce complexity with EMR.

Comprehensive and Detailed Explanation:

Amazon ECS on AWS Fargate: AWS Fargate is a serverless compute engine for containers that works with Amazon ECS. It allows you to run containers without managing servers or clusters.

Amazon EFS: Amazon Elastic File System (EFS) provides a simple, scalable, fully managed elastic NFS file system for use with AWS Cloud services and on-premises resources. It can be mounted to ECS tasks running on Fargate, allowing containers to access a shared file system with standard file system semantics, including OS-level permissions.

Comprehensive and Detailed Explanation From Exact Extract of Amazon Web Services (AWS) Architect documents:

To optimize costs for long-term data storage, AWS recommends using S3 Lifecycle policies to automate transitions across storage classes and ultimately expire old data.

S3 Standard-IA is suited for data that is accessed less frequently but requires millisecond retrieval times. It is ideal for 6-month-old data still used in monthly reports.

S3 Glacier Deep Archive is the lowest-cost option, designed for data accessed once or twice a year, such as regulatory audits — perfect for 2+ year-old data.

Lifecycle expiration rules allow S3 to automatically delete objects older than 7 years, aligning with the business retention policy.

These transitions are cost-effective and fully automated, aligning with the Cost Optimization pillar in the AWS Well-Architected Framework.

???? Reference:

S3 Storage Class Comparison

Lifecycle Management

Comprehensive and Detailed Explanation:

To track costs associated with different teams within a single AWS account, the company should implement user-defined cost allocation tags.

Tagging Resources with CostCenter: Assign the CostCenter tag to all resources, specifying the appropriate value for each team. This practice enables the organization to categorize and track AWS costs effectively.

Activating the CostCenter Tag: After tagging resources, activate the CostCenter tag in the AWS Billing and Cost Management console. Activation is necessary for the tags to appear in cost allocation reports and AWS Cost Explorer.

By following these steps, the company can generate detailed billing reports that break down costs by team, facilitating better cost management and accountability.

Amazon Kinesis Data Streams in on-demand mode is purpose-built for real-time ingestion of streaming data and scales automatically with traffic, which is ideal for fluctuating workloads like clickstream data.

Combined with AWS Lambda, which scales concurrently based on incoming events, this setup ensures efficient, real-time processing with minimal configuration and cost overhead.

Option B involves Firehose, which is not optimal for low-latency processing. Option C is incorrect as Kinesis Video Streams is designed for video data, not clickstream. Option D introduces Flink, which adds unnecessary complexity when Lambda suffices.

Comprehensive and Detailed Explanation From Exact Extract of Amazon Web Services (AWS) Architect documents:

While CloudWatch Network Monitor can monitor paths, it does not support ICMP directly. If you need ICMP-based RTT and packet loss tracking, you must implement it using scripts.

Using Lambda provides the lowest operational overhead:

Serverless

Scales automatically

No EC2 provisioning or maintenance

Can run scheduled ICMP checks using Amazon EventBridge

“You can schedule AWS Lambda to run at fixed intervals to monitor metrics such as latency or perform synthetic transactions.”

— AWS Lambda with CloudWatch Events

Incorrect Options:

A: Not currently capable of ICMP-level metrics.

B: EC2 instances add ongoing cost and overhead.

D: AWS Batch is overkill and more suited for compute-heavy jobs.

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.

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

Using API Gateway with API keys provides secure access control. Amazon SQS allows asynchronous decoupling between frontend and backend, ensuring that backend processing can scale independently. AWS Lambda reading from SQS ensures scalable, event-driven processing with minimal operational management. This architecture is resilient and decoupled.

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache for DynamoDB that delivers up to a 10x performance improvement—even at millions of requests per second. DAX requires minimal changes to existing applications and offloads the read workload from DynamoDB during report generation.

Reference Extract:

"DAX provides in-memory acceleration for DynamoDB tables, reducing response times from milliseconds to microseconds with minimal application changes."

Source: AWS Certified Solutions Architect – Official Study Guide, DynamoDB and Performance section.

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

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

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

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

AWS References:

Amazon API Gateway with DynamoDB

The issue in this case is related to the processing tier, where EC2 instances are overwhelmed at peak times, causing delays.Option D, using anAmazon EC2 Auto Scaling target tracking policybased on theApproximateNumberOfMessagesin the SQS queue, is the best solution.

Auto Scaling with Target Tracking:

Target tracking policiesdynamically scale out or in based on a specific metric. For this use case, you can monitor theApproximateNumberOfMessagesin the SQS queue. When the number of messages (orders) in the queue increases, the Auto Scaling group will scale out more EC2 instances to handle the additional load, ensuring that the queue doesn’t build up and cause delays.

This solution is ideal for handling variable and unpredictable peak times, as Auto Scaling can automatically adjust based on real-time load rather than scheduled times.

Why Not the Other Options?:

Option A (Scheduled Scaling): Scheduled scaling works well for predictable peak times, but this company experiencesunpredictable peak usage, making scheduled scaling less effective.

Option B (ElastiCache for Redis): Adding a caching layer would help if DynamoDB were the bottleneck, but in this case, the issue is CPU overload on EC2 instances in the processing tier.

Option C (CloudFront): CloudFront would help cache static content from the web tier, but it wouldn’t resolve the issue of the processing tier’s overloaded EC2 instances.

AWS References:

Amazon EC2 Auto Scaling Target Tracking

Amazon SQS ApproximateNumberOfMessages

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

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

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

TheAWS WAF Bot Control managed ruleis designed to automatically detect and mitigate bot traffic. This feature is particularly useful for addressing malicious traffic and conserving compute resources by filtering unnecessary requests at the ALB level.

Option A:Blocking IP addresses manually introduces significant operational overhead and is not scalable against dynamic, worldwide malicious traffic.

Option C:AWS Shield provides DDoS protection, but the scenario does not describe a DDoS attack. WAF is better suited for managing application-layer threats like bot traffic.

Option D:The AWS WAF IP reputation rule helps block traffic from known bad IPs but may not address bot traffic effectively.

AWS Documentation References:

AWS WAF Bot Control

AWS WAF Managed Rules

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.

This solution is the most cost-effective and efficient way to break down costs per application.

Tagging Resources: By tagging all AWS resources with a specific key (e.g., "cost") and a value representing the application's name, you can easily identify and categorize costs associated with each application. This tagging strategy allows for granular tracking of costs within AWS.

Activating Cost Allocation Tags: Once tags are applied to resources, you need to activate cost allocation tags in the AWS Billing and Cost Management console. This ensures that the costs associated with each tag are included in your billing reports and can be used for cost analysis.

AWS Cost Explorer: Cost Explorer is a powerful tool that allows you to visualize, understand, and manage your AWS costs and usage over time. You can filter and group your cost data by the tags you’ve applied to resources, enabling you to easily see the cost breakdown for each application. Cost Explorer also supports generating regular reports, which can be scheduled and emailed to stakeholders.

Why Not Other Options?:

Option A (AWS Budgets): AWS Budgets is more focused on setting cost and usage thresholds and monitoring them, rather than providing detailed cost breakdowns by application.

Option B (Load Cost and Usage Reports into RDS): This approach is less cost-effective and involves more operational overhead, as it requires setting up and maintaining an RDS instance and running SQL queries.

Option D (AWS Billing and Cost Management Console): While you can download bills, this method is more manual and less dynamic compared to using Cost Explorer with activated tags.

AWS References:

AWS Tagging Strategies- Overview of how to use tagging to organize and track AWS resources.

AWS Cost Explorer- Details on how to use Cost Explorer to analyze costs.

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

Amazon S3 is suitable for storing data that needs to be accessed weekly and integrates with AWS Key Management Service (KMS) to provide encryption at rest with server-side encryption using KMS-managed keys (SSE-KMS).

SSE-KMS uses envelope encryption and allows automatic key rotation and logging through AWS CloudTrail, satisfying the requirements for audit trails and compliance.

S3 Glacier Deep Archive is unsuitable due to its high retrieval latency. SSE-C requires customer-side management of encryption keys, with no support for automatic rotation or audit. SSE-S3 does not use customer-managed keys and lacks fine-grained control and auditing.

The company needs a cloud-based storage solution for frequently accessed data with low latency, while retaining their current on-premises infrastructure for some data storage. AWS Storage Gateway'sVolume Gateway with cached volumesis the most appropriate solution for this scenario.

AWS Storage Gateway - Volume Gateway (Cached Volumes):

Volume Gateway with cached volumesallows you to store frequently accessed data in the AWS Cloud while keeping the most recently accessed data cached locally on-premises. This ensures low-latency access to active data while providing scalability for the rest of the data in the cloud.

The cached volume option stores the primary data in Amazon S3 but caches frequently accessed data locally, ensuring fast access. This configuration is well-suited for applications that require fast access to frequently used data but can tolerate cloud-based storage for the rest.

Since the company is facing limited on-premises storage, cached volumes provide an ideal solution, as they reduce the need for additional on-premises storage infrastructure.

Why Not the Other Options?:

Option A (S3 File Gateway): S3 File Gateway provides a file-based interface (SMB/NFS) for storing data directly in S3. While it is great for file storage, the company’s need for block-level storage with iSCSI targets makes Volume Gateway a better fit.

Option C (Volume Gateway - Stored Volumes): Stored volumes keep all the data on-premises and asynchronously back up to AWS. This would not address the company's storage limitations since they would still need substantial on-premises storage.

Option D (Tape Gateway): Tape Gateway is designed for archiving and backup, not for frequently accessed low-latency data.

AWS References:

AWS Storage Gateway - Volume Gateway

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.

AWS WAF allows web applications to protect themselves from common web exploits and vulnerabilities. Using AWS managed rule groups ensures protection against known attack patterns, such as SQL injection and cross-site scripting. Associating AWS WAF with the ALB provides application-layer security and real-time threat mitigation.

Comprehensive and Detailed Explanation:

AWS Control Tower provides a straightforward way to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of a baseline environment, or landing zone, that includes:

Service Control Policies (SCPs): These are used to manage permissions across AWS Organizations, allowing you to set permission guardrails. SCPs can restrict access to specific AWS services and actions, helping enforce security best practices.

Multi-Factor Authentication (MFA): AWS Control Tower can enforce MFA for the root user across all accounts, enhancing security.

Centralized Governance: It offers centralized logging and monitoring, making it easier to manage and audit multiple AWS accounts.

Using a Lambda function as part of the Kinesis Data Firehose pipeline allows for real-time detection and masking of PII before data is written to S3. This ensures that PII is never stored in its raw form in the data lake.

Option B:Amazon Macie can scan and classify data but does not provide in-line PII masking for data ingestion.

Option C:Server-side encryption secures data but does not mask PII.

Option D:CloudHSM is unnecessary for PII masking and adds complexity without addressing the requirements.

AWS Documentation References:

Using Lambda with Kinesis Data Firehose

Amazon FSx for Lustreis a high-performance, fully managed file system that is ideal for applications requiring low-latency access to shared storage, especially in use cases like gaming where high throughput and low latency are essential. It integrates easily with EC2 instances, providing fast and scalable shared storage, and supports custom protocols for specific application needs.

Option A (FSx File Gateway): FSx File Gateway is designed for hybrid cloud storage and is not suited for high-performance gaming workloads.

Option B (EC2 Windows instance): Setting up a file share on a Windows instance would introduce additional administrative overhead and would not provide the necessary performance.

Option C (EFS with Lustre): While Lustre is integrated with FSx, EFS does not natively support Lustre.

AWS References:

Amazon FSx for Lustre

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

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

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

AWS References:

Amazon EventBridge API Destinations

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.

The best solution to query and filter S3 data directly without downloading the full object is to use Amazon Athena.

Amazon Athena is an interactive query service that lets you use SQL to analyze structured, semi-structured, and unstructured data directly in Amazon S3, without needing to move or transform the data.

It supports formats like CSV, JSON, ORC, Parquet, and Avro and integrates with AWS Glue Data Catalog for schema management.

Athena is serverless, meaning there’s no infrastructure to manage, and it's billed per query, which keeps it cost-effective.

Option B (EMR) is heavier and requires managing a cluster.

Option C (API Gateway) is not suited for querying S3 datasets.

Option D (ElastiCache) is a memory store, not a query engine.

???? Reference:

What is Amazon Athena?

CloudFront geographic restrictions (also known as geo-blocking) allow you to allow or deny content delivery to specific countries with minimal configuration.

“You can use geo restriction, also known as geoblocking, to prevent users in specific geographic locations from accessing content that you're distributing through a CloudFront web distribution.”

— CloudFront Geo Restriction

This is the most operationally efficient approach — no code, no signed URL logic.

Incorrect Options:

A/C: Signed URLs/cookies are for individual access control, not geo-blocking.

D: OAC controls access between CloudFront and S3, not to block specific countries.

Amazon RDS Proxy helps manage thousands of concurrent database connections by pooling and reusing them efficiently. It is especially useful for serverless applications like AWS Lambda that can open numerous connections quickly, potentially overwhelming the database. Using RDS Proxy reduces connection management overhead and improves fault tolerance.

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.

AWS Batch supports Windows-based jobs and automates provisioning and scaling of compute environments. Paired with AWS Fargate, it removes the need to manage infrastructure. This solution requires the least operational overhead and is cloud-native, providing flexibility and scalability.

To meet the requirement of deleting objects older than 3 years while retaining certain data, this solution leverages serverless technologies to minimize operational overhead.

S3 Inventory: S3 Inventory provides a flat file that lists all the objects in an S3 bucket and their metadata, which can be configured to include data such as the last modified date. This inventory can be generated daily or weekly.

AWS Lambda Function: A Lambda function can be created to process the S3 Inventory report, filtering out the objects that need to be retained and identifying those that should be deleted.

S3 Batch Operations: S3 Batch Operations can execute tasks such as object deletion at scale. By invoking the Lambda function through S3 Batch Operations, you can automate the process of deleting the identified objects, ensuring that the solution is serverless and requires minimal operational management.

Why Not Other Options?:

Option A (AWS CLI script on EC2): Running a script on an EC2 instance adds unnecessary operational overhead and is not serverless.

Option B (AWS Batch): AWS Batch is designed for running large-scale batch computing workloads, which is overkill for this scenario.

Option C (AWS Glue + script): AWS Glue is more suited for ETL tasks, and this approach would add unnecessary complexity compared to the serverless Lambda solution.

AWS References:

Amazon S3 Inventory- Information on how to set up and use S3 Inventory.

S3 Batch Operations- Documentation on how to perform bulk operations on S3 objects using S3 Batch Operations.

AWS Fargate is a serverless compute engine for containers that works with Amazon EKS. With Fargate, you do not need to provision or manage EC2 instances or clusters. You simply define and deploy your pods, and Fargate automatically launches the required compute resources. This results in the lowest operational overhead because AWS manages the infrastructure. Fargate profiles allow you to specify which pods run on Fargate.

AWS Documentation Extract:

“AWS Fargate is a serverless, pay-as-you-go compute engine that lets you focus on building applications without managing servers. With Amazon EKS and Fargate, you only need to define your application’s pods; Fargate provisions and manages the required compute resources for you.”

(Source: Amazon EKS documentation, AWS Fargate integration)

Other options:

A: Self-managed nodes require you to manage EC2 instances.

B: Managed node groups reduce some overhead, but you are still responsible for patching and managing the EC2 instances.

D: Managed node groups with Karpenter automate scaling but do not remove the need to manage underlying instances.

AWS CloudFormationallows for the automated, repeatable setup of infrastructure, reducing human error and ensuring consistency.AWS Configprovides the ability to track changes in the infrastructure, ensuring that all changes are logged and auditable, which satisfies the requirement for tracking incremental changes.

Option A and C (AWS Organizations): AWS Organizations manage multiple accounts, but they are not designed for infrastructure setup or change tracking.

Option D (Service Catalog): Service Catalog is used for deploying products, not for setting up infrastructure or tracking changes.

AWS References:

AWS Config

AWS CloudFormation

Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) is a fully managed, serverless service for real-time processing of streaming data. You can consume data from Kinesis Data Streams, perform anomaly detection, and then output the results to another Kinesis stream. This approach is loosely coupled, fully managed, and does not require modifying the application code.

AWS Documentation Extract:

“Amazon Managed Service for Apache Flink enables you to process streaming data in real time, integrating with Kinesis Data Streams as source and sink, and is fully managed and serverless.”

(Source: Apache Flink on AWS documentation)

B: S3/Redshift is not real-time and adds complexity.

C, D: EC2/ECS solutions are not serverless or fully managed.

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.

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

Aurora Global Database: Provides cross-Region disaster recovery with minimal data loss (<1 second replication latency).

S3 Cross-Region Replication (CRR): Automatically replicates data between buckets in different Regions.

“Aurora Global Database replicates your data with typically under one second of latency to secondary Regions.”

“Amazon S3 Cross-Region Replication automatically replicates objects across buckets in different AWS Regions.”

— Aurora Global Database

— S3 Cross-Region Replication

This meets the multi-Region DR requirement with minimal data loss.

ECS with Fargate: Allows containerized workloads to scale rapidly without managing underlying servers, handling unpredictable growth effectively.

RDS for Relational Data: Manages large relational datasets efficiently while supporting high availability.

SQS for Decoupling: Ensures message processing occurs in a specific order, decoupling application components and allowing independent evolution.

AWS ECS with Fargate Documentation,AWS SQS Documentation

AWS Fargate with Spot capacity is the most cost-effective and serverless container option for short-duration jobs that are flexible on start time. Since the job is not latency-critical and runs nightly, it is a good candidate for Fargate Spot, which can offer up to 70% cost savings over On-Demand pricing.

The job runs for 1 hour, which exceeds the AWS Lambda maximum execution time (15 minutes). Therefore, Lambda is not suitable. EC2-based solutions involve higher operational overhead and cost, making Fargate Spot the best low-cost, low-maintenance solution.

Comprehensive and Detailed Step-by-Step Explanation:

To meet the requirements of copying only relevant data as soon as possible and receiving notifications upon completion, the following steps are recommended:

Generate a Manifest File (Option A):

Action:Modify the on-premises data generation process to create a manifest file at the end of each data generation cycle. This manifest should list the names of the objects that need to be copied to Amazon S3.

Implementation:Develop a custom script that runs after data generation. This script compiles the list of relevant data files into a manifest file and uploads it to a designated S3 bucket.

Justification:Using a manifest allows AWS DataSync to transfer only the specified files, reducing unnecessary data transfer and associated costs.

docs.aws.amazon.com

Automate DataSync Task Execution (Option D):

Action:Set up an S3 Event Notification to trigger an AWS Lambda function whenever a new manifest file is uploaded to the S3 bucket.

Implementation:Configure the Lambda function to invoke the DataSync task by calling the StartTaskExecution API action, specifying the manifest file. This ensures that only the files listed in the manifest are copied from on-premises storage to Amazon S3.

Justification:This automation ensures timely data transfer as soon as relevant data is available, minimizing delays and manual intervention.

docs.aws.amazon.com

Set Up Completion Notifications (Option E):

Action:Create an Amazon SNS topic to handle notifications. Then, establish an Amazon EventBridge rule that monitors the DataSync task execution status and sends an email notification to the SNS topic when the status changes to SUCCESS or ERROR.

Implementation:Configure EventBridge to capture state changes of the DataSync task. When a task completes successfully or encounters an error, EventBridge triggers a notification to the SNS topic, which then sends an email to the subscribed recipients.

Justification:This setup provides immediate feedback on the data transfer process, allowing the analytics team to act promptly based on the success or failure of the data copy operation.

docs.aws.amazon.com

EMR runtime roles allow fine-grained permissions per job, letting each team access only the services they are authorized to use. This isolates IAM permissions per workload and avoids exposing instance-level credentials through IMDSv2. Runtime roles improve security posture in multi-tenant EMR environments.

Correct Approach:

AWS Security Groups:

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

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

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

Incorrect Options Analysis:

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

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

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

AWSSecrets Managercan integrate directly with Amazon RDS for automatic and seamless password rotation. Secrets Manager handles the complexity of password management, including generating strong passwords and rotating them at a defined interval (e.g., every 30 days). It also automatically updates the connection information for RDS, minimizing operational overhead.

Option A (Lambda with EventBridge): While possible, this requires custom coding and operational management of Lambda, which introduces additional complexity.

Option B (Manual password change): Using the modify-db-instance command requires manual intervention and is not automated, leading to more operational effort.

Option D (Parameter Store): Systems Manager Parameter Store is less specialized for password management than Secrets Manager and does not have built-in automated rotation for RDS credentials.

AWS References:

AWS Secrets Manager Rotation for RDS

Amazon Route 53 Resolver endpoints allow you to integrate DNS between AWS and on-premises environments easily. By creating inbound and outbound resolver endpoints, you can configure conditional forwarding rules so that DNS queries for your on-premises AD domain are forwarded to the on-premises DNS servers. This approach is fully managed, scales automatically, and requires the least administrative overhead.

AWS Documentation Extract:

"Route 53 Resolver provides DNS resolution between AWS and on-premises environments, using endpoints and forwarding rules to manage DNS query routing seamlessly."

(Source: Route 53 Resolver documentation)

A, D: Require provisioning, managing, and patching EC2 servers or domain controllers.

B: NS records in a private hosted zone do not provide true DNS forwarding.

TooManyRequestsException errors occur when Lambda exceeds concurrency limits. The recommended pattern is to use Amazon SQS with Lambda to decouple and buffer traffic, ensuring that bursts of requests are queued and processed smoothly. Enabling provisioned concurrency for Lambda ensures that functions are pre-initialized and ready to handle spikes in load with low latency. Step Functions (B) is designed for workflow orchestration, not high-throughput request buffering. SNS with Lambda (C) does not provide buffering and may overwhelm Lambda during bursts. Reserved concurrency (D) limits function scaling instead of improving resilience. Therefore, option A provides a scalable and resilient solution, minimizing errors during traffic surges.

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

AWS Storage Gateway Tape Gateway provides a seamless way to migrate backup data to AWS without requiring changes to the backup software. Migrating to S3 Glacier Deep Archive ensures long-term, cost-effective storage for data that rarely needs retrieval.

Option A:S3 Standard-IA is more expensive than Glacier for long-term storage.

Option B and D:Glacier Flexible Retrieval is costlier than Glacier Deep Archive for archival use cases with low retrieval frequency.

AWS Documentation References:

AWS Storage Gateway Tape Gateway

S3 Glacier Storage Classes

Comprehensive and Detailed Explanation From Exact Extract of Amazon Web Services (AWS) Architect documents:

The best practice for securely accessing AWS services from EC2 instances is to use IAM roles associated with the instance profile.

"Applications must securely access AWS resources. The recommended way is to attach an IAM role to your EC2 instance and use temporary credentials."

— IAM Roles for Amazon EC2

IAM roles:

Provide temporary, automatically rotated credentials.

Avoid hardcoding credentials.

Ensure least privilege access.

Incorrect Options:

A: Root credentials should never be used programmatically.

B: Using static access keys is not secure.

D: MFA and versioning help with object security but don’t handle programmatic access.

Enabling Multi-AZ deployment for an Amazon RDS DB instance is the simplest and most effective way to improve database availability and eliminate a single point of failure. Multi-AZ deployments provide automatic failover to a standby instance in case of a failure of the primary instance. This approach is managed by AWS and only requires a modification to the existing instance, with minimal manual intervention or downtime, thus providing the least implementation effort.

Reference Extract from AWS Documentation / Study Guide:

"With Multi-AZ deployments, Amazon RDS automatically creates a primary DB Instance and synchronously replicates the data to a standby instance in a different Availability Zone (AZ). In case of a failure of the primary DB Instance, Amazon RDS automatically fails over to the standby so that database operations can resume quickly."

Source: AWS Certified Solutions Architect – Official Study Guide, RDS High Availability section.

Private subnets require outbound internet access to download updates, but they must not have public IPs or direct inbound access.

The recommended AWS solution is to create a NAT gateway in a public subnet. Private subnet route tables are updated to route internet-bound traffic (0.0.0.0/0) to the NAT gateway. The NAT gateway then uses the internet gateway attached to the VPC to communicate with the internet.

Option B (NAT instances) is an older approach and less scalable/maintainable than NAT gateways. Option C (egress-only internet gateway) is for IPv6 outbound-only traffic, not IPv4. Option D is invalid because NAT gateways must be deployed in public subnets.