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

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.

AWS Lambda for Operational Efficiency:

AWS Lambdais a serverless compute service that allows you to run code without provisioning or managing servers. It automatically scales based on the number of invocations and eliminates the need to maintain and monitor EC2 instances, making it far more operationally efficient compared to running a cron job on EC2.

By using Lambda, you pay only for the compute time that your function uses. This is especially beneficial when dealing with lightweight tasks, such as scanning a DynamoDB table and sending email messages once a day.

Amazon DynamoDB:

DynamoDBis a highly scalable, fully managed NoSQL database. The table stores employee start dates, and scanning the table to find the employees who have a work anniversary on the current day is a lightweight operation. Lambda can easily perform this operation using theDynamoDB Scan APIor queries, depending on how the data is structured.

Amazon SNS for Email Notifications:

Amazon Simple Notification Service (SNS)is a fully managed messaging service that supports sending notifications to a variety of endpoints, including email. SNS is well-suited for sendingout email messages to employees, as it can handle the fan-out messaging pattern (sending the same message to multiple recipients).

In this scenario, once Lambda identifies employees who have their work anniversaries, it can use SNS to send the email notifications efficiently. SNS integrates seamlessly with Lambda, and sending emails via SNS is a common pattern for this type of use case.

Event Scheduling:

To automate this daily task, you can schedule the Lambda function usingAmazon EventBridge (formerly CloudWatch Events). EventBridge can trigger the Lambda function on a daily schedule (cron-like scheduling). This avoids the complexity and operational overhead of manually setting up cron jobs on EC2 instances.

Why Not EC2 or SQS?:

Option A & Bsuggest running a cron job on anAmazon EC2instance. This approach requires you to manage, scale, and patch the EC2 instance, which increases operational overhead. Lambda is a better choice because it automatically scales and doesn’t require server management.

Amazon Simple Queue Service (SQS)is ideal for decoupling distributed systems but isn’t necessary in this context because the goal is to send notifications to employees on their work anniversaries. SQS adds unnecessary complexity for this straightforward use case, whereSNSis the simpler and more efficient solution.

AWS References:

AWS Lambda

Amazon SNS

Amazon DynamoDB

Amazon EventBridge

Summary:

UsingAWS Lambdacombined withAmazon SNSto send notifications, and scheduling the function withAmazon EventBridgeto run daily, is the most operationally efficient solution. It leverages AWS serverless technologies, which reduce the need for infrastructure management and provide automatic scaling. Therefore,Option Cis the correct and optimal choice.

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

The correct solution isMulti-AZ deploymentbecause this is the Amazon RDS feature designed for high availability and fast failover. AWS prescriptive guidance shows Multi-AZ deployment as an in-Region DR option with an approximateRTO of 1–2 minutes, which is comfortably within the less-than-5-minute requirement. Read replicas are mainly for scaling reads, while snapshots and CloudFormation are not fast failover mechanisms. For a critical database workload that must remain highly available with minimal recovery time, Multi-AZ is the standard AWS-managed answer.

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A spread placement group is designed to deploy each instance on distinct underlying hardware, reducing the risk of simultaneous failures. By spreading instances across multiple Availability Zones, you achieve high availability and fault tolerance, meeting the 99.99% uptime requirement. Spread placement groups are ideal for critical applications that require maximum resilience to single hardware failures. Neither cluster nor partition strategies offer the same guarantee of separation combined with cross-AZ distribution.

Reference Extract from AWS Documentation / Study Guide:

" Spread placement groups are recommended for applications that have a small number of critical instances that should be kept separate from each other. Instances in a spread placement group are placed on distinct underlying hardware, and when deployed across multiple Availability Zones, provide high availability. "

Source: AWS Certified Solutions Architect – Official Study Guide, Compute and High Availability section; Amazon EC2 Placement Groups Documentation.

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

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

AWS Documentation Extract:

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

(Source: AWS S3 and SageMaker documentation)

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

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

Service control policies (SCPs) in AWS Organizations apply to the accounts or OUs to which they are attached:

To restrict only nonproduction accounts, you can:

Attach the SCP directly to those specific member accounts (Option B), or

Create a dedicated OU for the nonproduction accounts and attach the SCP to that OU (Option E).

Both methods ensure that the deny policy affects only the nonproduction accounts and does not impact the production account.

Why the others are incorrect:

A: Attaching the SCP to the root OU would apply the deny to all accounts, including production, which does not meet the requirement.

C: SCPs attached to the management account only affect that account, not all member accounts.

D: Creating an OU for the production account and attaching the deny SCP there would block the prohibited instance types in production, which is the opposite of what is required.

Interface VPC endpointsare the AWS-native way for workloads in private subnets to access supported AWS serviceswithout public IP addressesand without traversing the public internet. AWS documents that traffic through interface endpoints stays on the AWS network backbone and that instances in the VPC do not need public IP addresses to communicate with those service endpoints. NAT gateways still rely on public AWS service endpoints and are not the private-connectivity answer described in the question. Internet gateways are clearly incorrect for private-only access, and Direct Connect is unrelated to private subnet access to AWS managed services inside a VPC. Therefore, interface VPC endpoints are the right answer.

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Amazon SQS FIFO queues are built for workloads that require both message ordering and exactly-once processing behavior. AWS documentation states that FIFO queues preserve first-in, first-out order and support deduplication so duplicate messages are not introduced within the deduplication interval. That directly satisfies the requirement for exact order and exactly-once semantics. SNS alone is a pub/sub notification service, but it does not by itself provide this exact combination as simply as an SQS FIFO queue. Standard queues allow at-least-once delivery and can deliver messages out of order, so they do not meet the requirement. MSK could be engineered to solve the problem, but it introduces far more operational overhead than a managed queue service. For the most operationally efficient serverless design, SQS FIFO is the correct answer.

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

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

AWS 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.

The performance issue occurs because reporting queries compete with production traffic on the same primary database instance. The best-practice AWS solution is to offload read-heavy workloads to a separate database endpoint.

Option C adds an Amazon RDS read replica, which asynchronously replicates data from the primary instance. By redirecting reporting queries to the replica endpoint, the primary database can focus on transactional workloads, significantly improving application performance and customer experience.

Read replicas are specifically designed for this use case: scaling read capacity and isolating reporting or analytics queries. This solution requires minimal changes to the reporting job (endpoint update only) and avoids overprovisioning the primary database.

Option A (RDS Proxy) improves connection management but does not reduce query load or isolate reporting traffic. Option B is invalid because Multi-AZ does not scale reads and is not configurable across three AZs for a single instance. Option D increases instance size but does not address the underlying contention between workloads and increases cost unnecessarily.

Therefore, C is the most efficient and scalable solution to minimize reporting impact while maintaining high performance and availability.

Option Bis the correct solution because:

AWS Security Token Service (STS)allows the web application to request temporary security credentials that grant access to AWS resources. These temporary credentials are secure and short-lived, reducing the risk of misuse.

Using STS and IAM roles ensures scalability by enabling the application to dynamically assume roles with the required permissions for each AWS service.

Option A:Assigning IAM roles directly to instances is less flexible and would grant the same permissions to all applications on the instance, which is not ideal for a multi-service web application.Option C:AWS IAM Identity Center is used for managing single sign-on (SSO) for workforce users and is not designed for granting programmatic access to web applications.Option D:Storing long-term access keys, even in AWS Systems Manager Parameter Store, is less secure and does not scale well compared to temporary credentials from STS.

AWS Documentation References:

AWS Security Token Service (STS)

IAM Roles for Temporary Credentials

AWS Database Migration Service can migrate relational data toAmazon S3 in Apache Parquet format, which is a strong archival format for analytics. AWS also documents that Parquet in S3 can be queried byAmazon Athena, which removes the need to keep a database engine running just for annual reports. This is much lower-overhead than maintaining RDS or EC2 instances solely for occasional querying. Among the options, D is the only one that uses a serverless query service over archived relational data. Glacier Flexible Retrieval also fits archived data better than keeping a live relational instance. This makes D the most cost-effective and least operationally heavy approach. (docs.aws.amazon.com)

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Amazon Kinesis is the right streaming service because it ingests and processes data in real time. For the user-data store, Amazon DynamoDB is the correct database because AWS documents that DynamoDB is a serverless, fully managed, distributed NoSQL database with single-digit millisecond performance at any scale. That directly matches the durability, low-latency, and unknown-scale requirements in the question. RDS is not the most natural fit for unpredictable massive serverless scaling here, while CloudFront and Global Accelerator solve delivery and network-edge problems rather than real-time event ingestion plus low-latency persistence. For a serverless analytics application with streaming game data and highly scalable user storage, Kinesis and DynamoDB are the best pair.

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The correct approach is to attach anIAM roleto the EC2 instance profile and grant that role permission to read the secret fromAWS Secrets Manager. AWS documentation states that Secrets Manager uses IAM for authentication and authorization, and EC2 instance profiles are the supported mechanism for providing temporary credentials to applications running on EC2 instances. This avoids hardcoded credentials and removes the need for long-term access keys on the server. The other options either misuse IAM users, rely on the wrong access mechanism, or do not reflect how Secrets Manager permissions are normally granted to workloads.

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For the relational database tier, Amazon RDS Multi-AZ provides high availability with minimal manual intervention. In Multi-AZ mode, RDS maintains a synchronous standby in a different Availability Zone and provides automatic failover during certain failure scenarios. This reduces operational burden because the service handles replication, health monitoring, and failover orchestration.

For the container-based application tier, Amazon ECS with the Fargate launch type minimizes operational overhead because it removes the need to provision, patch, and scale the underlying EC2 instances that host containers. With Fargate, AWS manages the compute infrastructure, and the team focuses on task definitions, scaling policies, and application configuration. This supports a highly available, load-balanced architecture because ECS services can run tasks across multiple Availability Zones behind an Application Load Balancer, and scaling is managed via ECS Service Auto Scaling.

Option B describes read replicas, which are primarily used for scaling reads and, depending on configuration, may not provide the same automated failover characteristics as Multi-AZ for high availability. Read replicas are not a direct substitute for Multi-AZ HA. Option C (self-managed Docker cluster on EC2) is operationally heavy: you must manage node capacity, patching, cluster lifecycle, and scheduling. Option E (ECS on EC2) is a valid container platform but still requires managing EC2 instances, AMIs, and scaling/patching, which increases manual intervention compared to Fargate.

Therefore, combining RDS Multi-AZ (A) for database resilience and ECS Fargate (D) for serverless container operations meets the HA requirement with the least manual effort.

A Stored Volume Gateway stores the entire dataset locally on-premises while asynchronously and securely backing up the data to AWS. This meets the requirement for full local access and automatic transfer to AWS.

Cached Volume Gateway (Option C) keeps only frequently accessed data locally; the full dataset is stored in AWS, not on-premises.

Snowball and Snowball Edge (Options A and B) are migration tools, not ongoing backup solutions.

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CloudFront requires ACM certificates to be created in us-east-1. Origin Access Control (OAC) securely allows CloudFront to upload objects to S3 without exposing the bucket publicly. Transfer Acceleration and S3 website endpoints are unnecessary or incompatible for this use case.