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DVA-C02 · Question #663

A company has point-of-sale devices across thousands of retail shops that synchronize sales transactions with a centralized system. The system includes an Amazon API Gateway API that exposes an AWS…

The correct answer is D. Create an Amazon SQS queue. Publish transactions to the queue. Set the queue to invoke the. Decoupling the API from the database with an SQS queue smooths out spiky workloads - messages accumulate in quiet periods at almost zero cost, then are processed when traffic surges. By configuring SQS to invoke your Lambda function via an event source mapping and capping the…

Submitted by wei.xz· Mar 5, 2026Development with AWS Services

Question

A company has point-of-sale devices across thousands of retail shops that synchronize sales transactions with a centralized system. The system includes an Amazon API Gateway API that exposes an AWS Lambda function. The Lambda function processes the transactions and stores the transactions in Amazon RDS for MySQL. The number of transactions increases rapidly during the day and is near zero at night. How can a developer increase the elasticity of the system MOST cost-effectively?

Options

  • AMigrate from Amazon RDS to Amazon Aurora MySQL. Use an Aurora Auto Scaling policy to
  • BMigrate from Amazon RDS to Amazon Aurora MySQL. Use an Aurora Auto Scaling policy to
  • CCreate an Amazon SQS queue. Publish transactions to the queue. Set the queue to invoke the
  • DCreate an Amazon SQS queue. Publish transactions to the queue. Set the queue to invoke the

How the community answered

(32 responses)
  • A
    6% (2)
  • B
    13% (4)
  • C
    25% (8)
  • D
    56% (18)

Explanation

Decoupling the API from the database with an SQS queue smooths out spiky workloads - messages accumulate in quiet periods at almost zero cost, then are processed when traffic surges. By configuring SQS to invoke your Lambda function via an event source mapping and capping the function’s reserved concurrency below your RDS connection limit, you both protect the database from overload and let the queue buffer excess requests. This delivers highly elastic, cost-effective scaling without unnecessary overprovisioning.

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