DAS-C01 · Question #17
A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within…
The correct answer is A. For daily incoming data, use AWS Glue crawlers to scan and identify the schema. D. For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform E. For archived data, use Amazon EMR to perform data transformations. For daily transformations of 300 GB with varying file formats: (A) AWS Glue crawlers are purpose-built to scan S3 data, detect schemas across heterogeneous formats (CSV, JSON, Parquet, etc.), and populate the AWS Glue Data Catalog automatically - this is their primary use case…
Question
A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
- Daily transformations of 300 GB of data with different file formats
landing in Amazon S3 at a scheduled time.
- One-time transformations of terabytes of archived data residing in
the S3 data lake. Which combination of solutions cost-effectively meets the company's requirements for transforming the data? (Choose three.)
Options
- AFor daily incoming data, use AWS Glue crawlers to scan and identify the schema.
- BFor daily incoming data, use Amazon Athena to scan and identify the schema.
- CFor daily incoming data, use Amazon Redshift to perform transformations.
- DFor daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform
- EFor archived data, use Amazon EMR to perform data transformations.
- FFor archived data, use Amazon SageMaker to perform data transformations.
How the community answered
(26 responses)- A69% (18)
- B4% (1)
- C8% (2)
- F19% (5)
Explanation
For daily transformations of 300 GB with varying file formats: (A) AWS Glue crawlers are purpose-built to scan S3 data, detect schemas across heterogeneous formats (CSV, JSON, Parquet, etc.), and populate the AWS Glue Data Catalog automatically - this is their primary use case. (D) AWS Glue workflows with Glue jobs provide a fully managed, serverless, schedulable ETL pipeline ideal for the recurring daily transformation workload. For the one-time transformation of terabytes of archived data: (E) Amazon EMR with PySpark or other frameworks is designed for large-scale, one-time batch processing jobs at terabyte scale and is cost-effective for ephemeral clusters. Option B (Athena) is a query service, not a schema-cataloging ETL tool. Option C (Redshift) is a data warehouse, not suited for ETL of multi-format S3 files. Option F (SageMaker) is a machine learning platform, not a general data transformation tool.
Topics
Community Discussion
No community discussion yet for this question.