Amazon Web Services SAA-C03 Exam With Confidence Using Practice Dumps

The correct answer isAbecause the company needs to rotatedatabase credentials every 30 dayswhile maximizing security and minimizing development effort.AWS Secrets Manageris the AWS service specifically designed to store, manage, and rotate secrets such as database usernames and passwords. It supportsautomatic rotationfor supported databases, includingAmazon Aurora MySQL, which makes it the most secure and operationally efficient solution.

With Secrets Manager, credentials are encrypted at rest, access can be controlled through IAM policies, and applications can retrieve secrets programmatically without embedding them in source code or configuration files. Automatic rotation reduces the risk associated with static credentials and helps meet compliance requirements with minimal manual work. Because rotation is built into the service, the development effort is significantly lower than creating and maintaining custom rotation logic.

Option B can work, but it requires building and operating a customLambda rotation function, which adds complexity compared with the native automation available in Secrets Manager. Options C and D are not best practices because storing credentials in environment files or configuration files increases security risk and does not provide a managed secrets lifecycle.

AWS security best practices recommend centralizing sensitive credentials inAWS Secrets Managerand enablingautomatic rotationwhenever possible. This improves security posture, reduces operational overhead, and aligns directly with compliance requirements for regular credential changes. Therefore, storing Aurora MySQL credentials inSecrets Managerwith30-day automatic rotationis the best solution.

Option A is the best answer because it introduces both decoupling and horizontal scalability with minimal infrastructure management. API Gateway provides a managed front door for the microservices, Lambda provides serverless compute that scales with request volume, and SQS provides durable buffering between transactional stages such as order creation and payment processing. AWS recommends queue-based decoupling when producers and consumers operate at different speeds or when spikes must be absorbed without losing work. The EKS and EC2 alternatives add significantly more operational burden, and caching does not solve the core workflow-scaling issue. Therefore, a serverless microservices architecture using API Gateway, Lambda, and SQS is the best fit.

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Amazon DynamoDB supports two read consistency models: eventually consistent reads and strongly consistent reads. By default, DynamoDB uses eventual consistency, which means reads might not immediately reflect the most recent writes. This behavior explains why the application is not always receiving the latest data.

Because latency and performance are acceptable, the problem is not throughput or response time—it is data consistency. Therefore, the correct design change is to enable strongly consistent reads, which guarantee that reads always return the most up-to-date data after a successful write.

Option C is correct because strongly consistent reads ensure that the application sees the latest committed value for an item. This is critical for use cases such as inventory systems, user profiles, or transactional workflows where stale data can cause incorrect behavior.

Option A is invalid because DynamoDB does not support traditional read replicas. Option B does not affect consistency; GSIs are used to query data by alternative keys. Option D would worsen the problem because eventual consistency already causes stale reads.

Therefore, C is the appropriate recommendation to ensure correctness of returned data without changing the table schema or introducing additional complexity.