A company has deployed a new Amazon API Gateway API that retrieves the cost of items for the company’s online store. An AWS Lambda function supports the API and retrieves the data from an Amazon DynamoDB table. The API’s latency increases during times of peak usage each day. However, the latency of the DynamoDB table reads is constant throughout the day. A DevOps engineer configures DynamoDB Accelerator (DAX) for the DynamoDB table, and the API latency decreases throughout the day. The DevOps engineer then configures Lambda provisioned concurrency with a limit of two concurrent invocations. This change reduces the latency during normal usage. However, the company is still experiencing higher latency during times of peak usage than during times of normal usage. Which set of additional steps should the DevOps engineer take to produce the LARGEST decrease in API latency?

Topic #: - All AWS DevOps Engineer Professional Questions

A. Increase the read capacity of the DynamoDB table. Use AWS Application Auto Scaling to manage provisioned concurrency for the Lambda function.
B. Enable caching in API Gateway. Stop using provisioned concurrency for the Lambda function.
C. Delete the DAX cluster for the DynamoDB table. Use AWS Application Auto Scaling to manage provisioned concurrency for the Lambda function.
D. Enable caching in API Gateway. Use AWS Application Auto Scaling to manage provisioned concurrency for the Lambda function Most Voted

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