DVA-C02 · Question #112
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
The correct answer is A. Refactor the Lambda function into two functions. Configure one function to transform the data and. By breaking the Lambda function into two separate functions and using an SQS queue to hold the transformed data as messages, you can decouple the data transformation and loading processes. This allows for more controlled loading of data into the DynamoDB table and helps eliminate
Question
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?
Options
- ARefactor the Lambda function into two functions. Configure one function to transform the data and
- BTurn on auto scaling for the DynamoDB table. Use Amazon CloudWatch to monitor the table's
- CCreate an alias for the Lambda function. Configure provisioned concurrency for the application to
- DRefactor the Lambda function into two functions. Configure one function to store the data in the
How the community answered
(18 responses)- A72% (13)
- B17% (3)
- C6% (1)
- D6% (1)
Explanation
By breaking the Lambda function into two separate functions and using an SQS queue to hold the transformed data as messages, you can decouple the data transformation and loading processes. This allows for more controlled loading of data into the DynamoDB table and helps eliminate throttling issues.
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