Which solution will meet these requirements?
Configure provisioned concurrency on the Lambda function with a concurrency value of 1. Delete the DAX cluster for the DynamoDB table.
Configure reserved concurrency on the Lambda function with a concurrency value of 0.
Configure provisioned concurrency on the Lambda function. Configure AWS Application Auto Scaling on the Lambda function with provisioned concurrency values set to a minimum of 1 and a maximum of 100.
Configure reserved concurrency on the Lambda function. Configure AWS Application Auto Scaling on the API Gateway API with a reserved concurrency maximum value of 100.
Explanations:
While configuring provisioned concurrency can help with cold starts, setting it to a concurrency value of 1 may not handle peak loads effectively, and deleting the DAX cluster will eliminate the caching benefits it provides, potentially increasing latency.
Setting reserved concurrency to 0 effectively disables the Lambda function, meaning it cannot serve any requests. This would not reduce latency; rather, it would lead to application downtime during peak requests.
Configuring provisioned concurrency ensures that a specified number of instances are always warm, reducing cold start latency. Additionally, using AWS Application Auto Scaling allows for dynamic scaling during peak times, effectively managing high request loads and keeping latency low.
While reserved concurrency can limit the function’s concurrency, it does not directly address the cold start issue. Furthermore, setting reserved concurrency on API Gateway does not help in reducing the Lambda function’s cold start latency.
If I’m correct, the answer is:
Configure provisioned concurrency on the Lambda function. Configure AWS Application Auto Scaling on the Lambda function with provisioned concurrency values set to a minimum of 1 and a maximum of 100.