A company is modernizing a batch processing application. The application currently runs on a single large EC2 instance and takes 12 hours to process a nightly file. The processing logic can be parallelized. The company wants to move to a serverless architecture to reduce processing time and only pay for compute when the job is running. Which architecture BEST meets these requirements?

Answer options:

Use Amazon EC2 Auto Scaling to launch 10 instances at night, process the file, and terminate them.

Use Amazon S3 to trigger an AWS Lambda function that processes the entire file.

Use Amazon S3 to trigger an AWS Lambda function that splits the file. Use AWS Step Functions Map state to process the chunks in parallel using AWS Fargate.

Use AWS Glue to run a Python shell script to process the file.

How to approach this question

Look for serverless orchestration (Step Functions) and parallelization (Map state).

Full Answer

C.Use Amazon S3 to trigger an AWS Lambda function that splits the file. Use AWS Step Functions Map state to process the chunks in parallel using AWS Fargate.✓ Correct

AWS Step Functions includes a Map state designed for dynamic parallelism. By splitting the file and using Map to trigger AWS Fargate tasks (or Lambda functions) concurrently, a 12-hour sequential job can be reduced to minutes, using entirely serverless compute.

Common mistakes

Choosing Lambda for the whole file, forgetting the 15-minute timeout.

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