AWS Certified Solutions Architect – Associate (SAA-C02) — Question 528

A company is designing an application. The application uses an AWS Lambda function to receive information through Amazon API Gateway and to store the information in an Amazon Aurora PostgreSQL database.
During the proof-of-concept stage, the company has to increase the Lambda quotas significantly to handle the high volumes of data that the company needs to load into the database. A solutions architect must recommend a new design to improve scalability and minimize the configuration effort.
Which solution will meet these requirements?

Answer options

• A. Refactor the Lambda function code to Apache Tomcat code that runs on Amazon EC2 instances. Connect the database by using native Java Database Connectivity (JDBC) drivers.
• B. Change the platform from Aurora to Amazon DynamoDB. Provision a DynamoDB Accelerator (DAX) cluster. Use the DAX client SDK to point the existing DynamoDB API calls at the DAX cluster.
• C. Set up two Lambda functions. Configure one function to receive the information. Configure the other function to load the information into the database. Integrate the Lambda functions by using Amazon Simple Notification Service (Amazon SNS).
• D. Set up two Lambda functions. Configure one function to receive the information. Configure the other function to load the information into the database. Integrate the Lambda functions by using an Amazon Simple Queue Service (Amazon SQS) queue.

Correct answer: D

Explanation

Using an Amazon SQS queue to decouple the ingestion and database-loading Lambda functions allows the system to buffer incoming messages and process them at a manageable, throttled rate. This prevents database connection exhaustion and eliminates the need to scale Lambda concurrency quotas excessively. In contrast, Amazon SNS does not buffer messages to throttle processing, and migrating to EC2 or DynamoDB would require significant migration and configuration effort.