SAA-C03 Exam Dumps - AWS Certified Solutions Architect - Associate (SAA-C03)

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.

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

AWS Secrets Manager is designed specifically to securely store and manage sensitive information such as database credentials. It integrates seamlessly with AWS services like Lambda and RDS, and it provides automatic credential rotation with minimal operational overhead.

AWS Secrets Manager: By storing the database credentials in Secrets Manager, you ensure that the credentials are securely stored, encrypted, and managed. Secrets Manager provides a built-in mechanism to automatically rotate credentials at regular intervals (e.g., every 30 days), which helps in maintaining security best practices without requiring additional manual intervention.

Lambda Integration: The Lambda function can be easily configured to retrieve the credentials from Secrets Manager using the AWS SDK, ensuring that the credentials are accessed securely at runtime.

Why Not Other Options?:

Option A (Parameter Store with Rotation): While Parameter Store can store parameters securely, Secrets Manager is more tailored for secrets management and automatic rotation, offering more features and less operational overhead.

Option C (Encrypted Lambda environment variable): Storing credentials directly in Lambda environment variables, even when encrypted, requires custom code to manage rotation, which increases operational complexity.

Option D (KMS with automatic rotation): KMS is for managing encryption keys, not for storing and rotating secrets like database credentials. This option would require more custom implementation to manage credentials securely.

AWS References:

AWS Secrets Manager- Detailed documentation on how to store, manage, and rotate secrets using AWS Secrets Manager.

Using Secrets Manager with AWS Lambda- Guidance on integrating Secrets Manager with Lambda for secure credential management.

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.

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

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.