AWS Certified Data Analytics – Specialty — Question 31
A company is planning to do a proof of concept for a machine learning (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?
Answer options
- A. Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluster. Store the curated data in Amazon S3 for ML processing.
- B. Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.
- C. Ingest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.
- D. Take a full backup of the data store and ship the backup files using AWS Snowball. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.
Correct answer: C
Explanation
The correct answer is C because using AWS DMS to ingest data into Amazon S3 combined with AWS Glue for data curation is efficient and takes advantage of fully managed services optimized for these tasks. Option A involves additional complexity with Apache Spark and EMR, which may not be as fast. Option B requires custom ETL jobs on-premises, which can be slower and less scalable. Option D is less efficient due to the manual backup and shipping process, resulting in longer delays.