A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days. The company has an Amazon

Question

Options

• ACreate an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate
• BCreate an AWS Lambda function that scans the S3 bucket. Program the Lambda function to
• CCreate an Amazon EventBridge rule that has an event pattern that matches the S3 upload.
• DUse Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline

• A
  3% (1)
• B
  9% (3)
• C
  74% (26)
• D
  14% (5)

Explanation

Option C is correct because Amazon EventBridge can natively detect S3 PutObject events and directly trigger a SageMaker Pipeline execution - no custom code, no polling, and no intermediary infrastructure required. This is a fully managed, event-driven pattern that requires minimal setup and zero ongoing maintenance.

Why the distractors are wrong:

• A - S3 Lifecycle rules manage object transitions (e.g., moving data to Glacier) and deletions; they cannot trigger compute pipelines. This is a fundamental misuse of the feature.
• B - A Lambda function that scans (polls) the bucket introduces unnecessary operational overhead: you must schedule it, manage state to detect "new" files, and maintain the function. It works, but it's not the least-effort path.
• D - MWAA (Managed Airflow) is a powerful orchestration tool but is heavyweight for this use case. It requires provisioning an Airflow environment, writing DAG code, and managing the service - far more operational effort than a single EventBridge rule.

Memory tip: Think "event = EventBridge." Whenever an AWS exam asks "trigger X when Y happens in S3/SNS/DynamoDB" with the constraint of least operational effort, EventBridge is almost always the answer - it's the native AWS event bus that wires services together without custom code.

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