MLS-C01 exam dumps

MLS-C01 practice question 60 of 389

AWS Certified Machine Learning - Specialty. Expert level, Amazon Web Services. Free question with the correct answer and a full explanation.

MLS-C01 Question 60

Single answer

You are working for an e-commerce company, and you need to process large volumes of clickstream data in near real-time to identify customer behavior patterns. The data is ingested into Amazon S3 in raw format. You want to transform this data into a structured format and load it into Amazon Redshift for analysis. Which AWS service or combination of services is best suited for this ETL task?

  1. A

    AWS Glue

  2. B

    Amazon EMR

  3. C

    AWS Batch

  4. D

    Amazon Kinesis Data Analytics

Show answer and explanation

Correct answer: A

Explanation

AWS Glue is the most suitable service for this scenario as it simplifies the ETL process by providing a serverless, fully managed environment for discovering, transforming, and loading data. It integrates seamlessly with Amazon S3 and Amazon Redshift, making it the optimal choice for structuring the raw clickstream data and loading it into Amazon Redshift for analysis.

  • A. Correct.

    AWS Glue is a fully managed ETL service that can crawl, catalog, transform, and load data into various destinations like Amazon Redshift. It is highly suitable for transforming and structuring data stored in Amazon S3.

  • B. Incorrect.

    Amazon EMR is capable of processing large-scale data using distributed frameworks like Apache Spark and Hadoop, but it is more complex to manage compared to AWS Glue for ETL tasks. It is not the best choice for this scenario.

  • C. Incorrect.

    AWS Batch is designed for batch processing workloads and is not specifically tailored for ETL tasks involving data transformation and loading into Amazon Redshift.

  • D. Incorrect.

    Amazon Kinesis Data Analytics is a managed service for processing streaming data, but the scenario involves data already stored in Amazon S3, making it unsuitable for this use case.

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