Topic 1 Question 603
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 IoT 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?
Use the AWS Step Functions Map state in Inline mode to process the data in parallel.
Use the AWS Step Functions Map state in Distributed mode to process the data in parallel.
Use AWS Glue to process the data in parallel.
Use several AWS Lambda functions to process the data in parallel.
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- 正解だと思う選択肢: B
AWS Step Functions allows you to orchestrate and scale distributed processing using the Map state. The Map state can process items in a large dataset in parallel by distributing the work across multiple resources. Using the Map state in Distributed mode will automatically handle the parallel processing and scaling. Step Functions will add more workers to process the data as needed. Step Functions is serverless so there are no servers to manage. It will scale up and down automatically based on demand.
👍 5Guru4Cloud2023/09/23 - 正解だと思う選択肢: B
With Step Functions, you can orchestrate large-scale parallel workloads to perform tasks, such as on-demand processing of semi-structured data. These parallel workloads let you concurrently process large-scale data sources stored in Amazon S3. https://docs.aws.amazon.com/step-functions/latest/dg/concepts-orchestrate-large-scale-parallel-workloads.html
👍 2taustin22023/09/22 Large Scale + Parallel = Distributed Step Function
https://docs.aws.amazon.com/step-functions/latest/dg/concepts-inline-vs-distributed-map.html
👍 1[Removed]2023/09/22
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