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…

Question

Options

• AUse AWS Glue DataBrew to import the data into SageMaker AI.
• BBuild a pipeline in SageMaker Pipelines to process the data. Use AWS DataSync to load the
• CCreate a feature store in SageMaker Feature Store. Use an Apache Spark connector to Feature
• DUse SageMaker Data Wrangler to query and import the data.

• A
  13% (6)
• B
  4% (2)
• C
  4% (2)
• D
  78% (36)

Explanation

Amazon SageMaker Data Wrangler is specifically designed for data ingestion, exploration, and preparation inside SageMaker AI. It includes built-in native connectors for Amazon S3, Amazon Athena, and Snowflake (among dozens of other sources), allowing an ML engineer to query, preview, and import data from all three sources directly into a SageMaker environment without writing custom ETL code. Option A (AWS Glue DataBrew) is a standalone data preparation service and does not natively integrate all three sources into SageMaker in one workflow. Option B (SageMaker Pipelines + AWS DataSync) is incorrect because DataSync is a file-transfer service for migrating data to AWS storage, not a query engine for Athena or Snowflake. Option C (SageMaker Feature Store + Apache Spark) is an engineering-heavy approach that requires a custom Spark connector setup and is far more complex than necessary for simply loading historical data.

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