AWS Certified Developer – Associate (DVA-C02) — Question 75

A developer has designed an application to store incoming data as JSON files in Amazon S3 objects. Custom business logic in an AWS Lambda function then transforms the objects, and the Lambda function loads the data into an Amazon DynamoDB table. Recently, the workload has experienced sudden and significant changes in traffic. The flow of data to the DynamoDB table is becoming throttled.

The developer needs to implement a solution to eliminate the throttling and load the data into the DynamoDB table more consistently.

Which solution will meet these requirements?

Answer options

• A. Refactor the Lambda function into two functions. Configure one function to transform the data and one function to load the data into the DynamoDB table. Create an Amazon Simple Queue Service (Amazon SQS) queue in between the functions to hold the items as messages and to invoke the second function.
• B. Turn on auto scaling for the DynamoDB table. Use Amazon CloudWatch to monitor the table's read and write capacity metrics and to track consumed capacity.
• C. Create an alias for the Lambda function. Configure provisioned concurrency for the application to use.
• D. Refactor the Lambda function into two functions. Configure one function to store the data in the DynamoDB table. Configure the second function to process the data and update the items after the data is stored in DynamoDB. Create a DynamoDB stream to invoke the second function after the data is stored.

Correct answer: A

Explanation

Option A is correct because it allows for better handling of sudden traffic spikes by decoupling the data transformation and loading processes, using SQS to manage message flow. Option B only addresses scaling the database but does not improve the data loading process. Option C focuses on concurrency without addressing the underlying issue of data processing flow. Option D introduces additional complexity without properly managing the data flow like option A does.