Q25 — AWS SAA-C03 Ch.6

Question 25 of 65 | ← Chapter 6

Q390. A company recently migrated to the AWS Cloud. The company wants a serverless solution for large-scale parallel on-demand processing of a semistructured dataset. The data consists of logs. media files. Sales transactions, and loT sensor data that is stored in Amazon S3. The company wants the solution to process thousands of items in the dataset in parallel.Which solution will meet these requirements with the MOST operational efficiency?

Correct Answer: B. Use the AWS Step Functions Map state in Distributed mode to process the data in parallel

Explanation

To achieve a serverless solution for large-scale parallel on-demand processing of a semi-structured dataset consisting of logs, media files, sales transactions, and IoT sensor data stored in Amazon S3 with the highest operational efficiency, the most suitable solution is: B. Use the AWS Step Functions Map state in Distributed mode to process the data in parallel. Option B suggests using the AWS Step Functions Map state in Distributed mode. AWS Step Functions is a serverless workflow service that allows you to coordinate and orchestrate multiple AWS services. The Map state enables parallel processing of items in a collection. By using the Map state in Distributed mode, the company can achieve high operational efficiency for parallel processing of thousands of items in the dataset. The Map state automatically divides the workload across multiple instances, managing the parallelism and scalability for you. This helps ensure efficient utilization of resources and faster processing times. Option A suggests using the AWS Step Functions Map state in Inline mode. While Inline mode can also process data in parallel, it may not be as operationally efficient as Distributed mode for large-scale processing. Distributed mode provides better scalability and resource management. Option C suggests using AWS Glue to process the data in parallel. While AWS Glue is a powerful data integration service, it is primarily designed for Extract, Transform, Load (ETL) processes and data cataloging. It may not be the most efficient solution for large-scale parallel processing of a dataset with different types of data. Option D suggests using several AWS Lambda functions to process the data in parallel. While Lambda functions are capable of parallel processing, managing and orchestrating multiple Lambda functions for large-scale parallel processing can introduce complexity in coordination and monitoring. Using AWS Step Functions with the Map state in Distributed mode provides a more streamlined and efficient approach.