Which set of additional steps should the DevOps engineer take to produce the LARGEST decrease in API latency?
Increase the read capacity of the DynamoDB table. Use AWS Application Auto Scaling to manage provisioned concurrency for the Lambda function.
Enable caching in API Gateway. Stop using provisioned concurrency for the Lambda function.
Delete the DAX cluster for the DynamoDB table. Use AWS Application Auto Scaling to manage provisioned concurrency for the Lambda function.
Enable caching in API Gateway. Use AWS Application Auto Scaling to manage provisioned concurrency for the Lambda function
Explanations:
Increasing the read capacity of the DynamoDB table may not provide a significant improvement in latency, as the DynamoDB reads are already constant. Scaling Lambda concurrency may help during peak usage, but it doesn’t address the root cause of API latency effectively.
Disabling provisioned concurrency for Lambda will reduce the performance improvements that were achieved by configuring it, which is counterproductive. API Gateway caching could help with repeated API requests, but removing Lambda provisioned concurrency could negate gains during peak times.
Deleting the DAX cluster would remove the improvement made to the DynamoDB table’s performance, which is beneficial for read-intensive operations. Scaling Lambda concurrency alone would not address the peak usage latency effectively without optimizing other components.
Enabling API Gateway caching can significantly reduce API latency by caching frequent responses. Using AWS Application Auto Scaling for Lambda ensures that Lambda has enough capacity during peak usage, providing the largest decrease in API latency during those times.