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A company processes a CSV file that contains millions of transaction records every day.

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A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation.

Which solution will meet these requirements with the LEAST operational overhead?

A.

Create an AWS Batch job that processes chunks of the file in parallel with a custom error tracking mechanism.

B.

Use AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to 15%.

C.

Deploy an Amazon EMR cluster with Spark to process the file. Configure a custom failure threshold to 15%.

D.

Use AWS Lambda with S3 Batch Operations to process the file and track validation failures to be less than 15%.

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