An application uses an AWS Lambda function (invoked via Amazon API Gateway) to store data in an Amazon Aurora PostgreSQL database. During proof-of-concept, the company raised Lambda quotas to handle high-volume data loads. A solutions architect must recommend a design that improves scalability and minimizes configuration effort. Which solution meets these requirements?

The correct solution uses an SQS queue to decouple the receiving Lambda function from the database loading Lambda function. This improves scalability because the receiving Lambda can quickly put messages onto the queue, allowing it to handle high-volume bursts without being bottlenecked by database write speeds or Lambda concurrency limits for the loading function. SQS automatically scales to handle large message volumes, and the loading Lambda can process messages at its own pace, retrying failures as needed. This minimizes configuration effort compared to managing EC2 instances or refactoring for a different database. Refactoring to EC2 and JDBC introduces operational overhead and doesn't inherently improve scalability for burst loads as effectively as a queue. Changing to DynamoDB and DAX is a significant database migration and refactoring effort, not a minimal configuration change, and might not be suitable for existing PostgreSQL data. Integrating with SNS would fan out messages, but SNS is