MLS-C01 Question 42
Single answerYou are working as a machine learning engineer at a company that processes large volumes of semi-structured data from multiple sources, such as JSON files and relational databases. You need to prepare this data for training a machine learning model by transforming and aggregating it into a clean, structured format. The solution must minimize operational overhead and allow for automated schema inference. Which AWS Glue feature should you leverage in this scenario?
- A
AWS Glue Data Catalog
- B
AWS Glue ETL Jobs
- C
AWS Glue Crawlers
- D
AWS Glue Studio
Show answer and explanation
Correct answer: C
Explanation
AWS Glue Crawlers are specifically designed to automatically scan and infer schemas from various data sources, including semi-structured formats like JSON. By using crawlers, you can minimize operational overhead and ensure that your data is ready for downstream processing, such as ETL transformations or machine learning workflows. This makes them the best choice for this scenario.
- A. Incorrect.
AWS Glue Data Catalog is used to store metadata about datasets but does not handle transforming or aggregating data.
- B. Incorrect.
AWS Glue ETL Jobs are used to perform data transformations, but they require you to define the schema or transformations explicitly. They do not infer schemas automatically.
- C. Correct.
AWS Glue Crawlers automatically scan data sources, infer schemas, and populate the Glue Data Catalog. This makes them ideal for processing semi-structured data with minimal manual effort.
- D. Incorrect.
AWS Glue Studio provides a visual interface for designing ETL workflows but does not specifically handle tasks like schema inference or data aggregation directly.