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Answer: Split the Lambda into two, using an SQS queue for data transformation and loading into DynamoDB.
Option A is correct because it involves refactoring the Lambda function into two separate functions and using an Amazon Simple Queue Service (SQS) queue to hold the transformed data as messages. This decouples the data transformation and loading processes, allowing for a more controlled and consistent loading of data into the DynamoDB table. This method helps eliminate throttling issues by smoothing out spikes in traffic and ensuring that data is loaded at a manageable rate.
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An application stores JSON data in Amazon S3, processed by a Lambda function before loading into a DynamoDB table. With fluctuating traffic causing throttling issues, what strategy should be implemented to consistently load data without throttling?
A
Split the Lambda into two, using an SQS queue for data transformation and loading into DynamoDB.
B
Enable DynamoDB auto scaling and monitor capacity with Amazon CloudWatch.
C
Use Lambda function aliases with provisioned concurrency.
D
Refactor Lambda into two, with one for data storage in DynamoDB and the other for post-storage data processing via a DynamoDB stream.