DAS-C01 · Question #44
A company is planning to do a proof of concept for a machine earning (ML) project using Amazon SageMaker with a subset of existing on-premises data hosted in the company's 3 TB data warehouse. For…
The correct answer is C. Ingest data into Amazon S3 using AWS DMS. The goal is the fastest path to getting on-premises database data into AWS for ML data curation. AWS DMS (Database Migration Service) is purpose-built to migrate relational database data directly to Amazon S3 quickly using the established Direct Connect link, making it the…
Question
A company is planning to do a proof of concept for a machine earning (ML) project using Amazon SageMaker with a subset of existing on-premises data hosted in the company's 3 TB data warehouse. For part of the project, AWS Direct Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data analysts want to perform multiple step, including mapping, dropping null fields, resolving choice, and splitting fields. The company needs the fastest solution to curate the data for this project. Which solution meets these requirements?
Options
- AIngest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the
- BCreate custom ETL jobs on-premises to curate the data.
- CIngest data into Amazon S3 using AWS DMS.
- DTake a full backup of the data store and ship the backup files using AWS Snowball.
How the community answered
(43 responses)- A19% (8)
- B5% (2)
- C65% (28)
- D12% (5)
Explanation
The goal is the fastest path to getting on-premises database data into AWS for ML data curation. AWS DMS (Database Migration Service) is purpose-built to migrate relational database data directly to Amazon S3 quickly using the established Direct Connect link, making it the fastest and most direct ingestion method for a 3 TB data warehouse. Once in S3, AWS Glue natively supports all the curation operations mentioned - mapping, dropping null fields, resolving choice conflicts (ResolveChoice transform), and splitting fields - through its DynamicFrames API. Option A uses AWS DataSync, which is optimized for file system transfers, not database migrations, adding unnecessary complexity. Option B (custom on-premises ETL) is slow and defeats the purpose of moving to AWS. Option D (Snowball) requires physical hardware shipping, which introduces days of delay - clearly not the fastest solution.
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