SAA-C03 Q131 · Serverless

Question

A company is using microservices to build an ecommerce application on AWS. The company wants to preserve customer transaction information after customers submit orders. The company wants to store transaction data in an Amazon Aurora database. The company expects sales volumes to vary throughout each year.

Accepted Answer

Option A satisfies every requirement:

1. **Preserve transaction data**: SQS acts as a durable buffer between the customer request and the database write. Even if Aurora is temporarily unavailable, messages stay in the queue — transactions are never lost.

2. **Dead-letter queue (DLQ)**: If a message fails processing repeatedly (e.g., malformed data, transient DB errors), it moves to the DLQ instead of being discarded. This ensures zero data loss and provides an avenue for debugging and reprocessing.

3. **Handle varying sales volumes**: This is the critical requirement. Lambda + SQS scales automatically:
   - API Gateway and the first Lambda handle incoming bursts by quickly placing messages onto SQS.
   - SQS absorbs traffic spikes (essentially infinite throughput for standard queues).

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