AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 211

An ML engineer is building an ML model in Amazon SageMaker AI. The ML engineer needs to load historical data directly from Amazon S3, Amazon Athena, and Snowflake into SageMaker AI.

Which solution will meet this requirement?

Answer options

• A. Use AWS Glue DataBrew to import the data into SageMaker AI.
• B. Build a pipeline in SageMaker Pipelines to process the data. Use AWS DataSync to load the processed data into SageMaker AI.
• C. Create a feature store in SageMaker Feature Store. Use an Apache Spark connector to Feature Store to access the data.
• D. Use SageMaker Data Wrangler to query and import the data.

Correct answer: D

Explanation

The correct answer is D because SageMaker Data Wrangler is specifically designed to assist in data preparation and allows for querying and importing data from various sources like Amazon S3 and Snowflake. Options A, B, and C do not provide a direct mechanism for importing data as efficiently as Data Wrangler does in this context.