A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data. The data scientist saves the transformations to SageMaker…

Question

Options

• AUse AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on
• BRun an AWS Step Functions step and a predefined SageMaker pipeline to perform the
• CUse Apache Airflow to orchestrate a set of predefined transformations on each new dataset that
• DConfigure Amazon EventBridge to run a predefined SageMaker pipeline to perform the

• A
  10% (4)
• B
  5% (2)
• C
  3% (1)
• D
  82% (32)

Explanation

Amazon EventBridge natively integrates with Amazon S3 and SageMaker Pipelines. When new historical data lands in the S3 bucket, an EventBridge rule can automatically trigger a predefined SageMaker pipeline that runs the Data Wrangler transformations and writes results to Feature Store - all with zero custom application code. This is a fully managed, event-driven, serverless solution using native AWS service integrations. Lambda (A) could invoke a pipeline but requires writing and maintaining Lambda function code. Step Functions (B) adds orchestration overhead that isn't needed when SageMaker Pipelines already handles the workflow. Apache Airflow (C) is a third-party orchestration tool that requires provisioning, managing an Airflow environment, and writing DAGs - far more operational effort than EventBridge.

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