A company is developing a platform to process large volumes of data for complex analytics and machine learning (ML) tasks. The platform must handle compute-intensive workloads. The workloads currently

Question

Options

• ADeploy three Amazon EC2 instances. Distribute the EC2 instances across three Availability
• BCreate an Amazon EMR cluster. Use managed scaling. Install Apache Spark to assist with data
• CCreate an AWS Lambda function for each data processing step. Deploy an Amazon Simple
• DCreate a series of AWS Lambda functions to process the data. Use AWS Step Functions to

• A
  17% (7)
• B
  71% (30)
• C
  5% (2)
• D
  7% (3)

Explanation

Amazon EMR provides a managed big data framework that supports Apache Spark, which is ideal for distributed and compute-intensive data transformations. Managed scaling dynamically adjusts cluster resources, ensuring high performance with minimal management. "Amazon EMR provides a managed environment for big data frameworks such as Apache Spark and Hadoop. With managed scaling, EMR automatically resizes clusters to meet workload Why B is correct: Provides distributed parallel processing for large datasets. Reduces operational overhead with managed scaling and auto-termination. Integrates easily with S3, Glue, and ML pipelines. Optimized for heavy ETL and analytics workloads. Why others are incorrect: A: Manual scaling and limited processing capacity. C & D: Lambda has execution time and memory limits unsuitable for 30-minute compute- intensive tasks.

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