A vendor delivers cleaned, ready-to-use training data to the company's Amazon S3 bucket every 3–4 days. The company has a SageMaker pipeline to retrain the model. The ML engineer needs the pipeline to run automatically when new data arrives in S3 with the least operational overhead. Which solution should they implement?

The correct solution is to create an Amazon EventBridge rule that matches S3 object-created events and configures the SageMaker pipeline as the rule target. EventBridge is a serverless event bus that can react to events from AWS services like S3. When new data is uploaded to the specified S3 bucket, an S3 object-created event is generated. EventBridge can then detect this event and directly trigger the SageMaker pipeline, providing an automated and low-overhead solution. Incorrect options: S3 Lifecycle rules are for managing object storage classes or deletion, not for triggering compute services like SageMaker pipelines. Deploying an AWS Lambda function to scan the S3 bucket is less efficient and has higher operational overhead than using EventBridge's native S3 event integration. Lambda would need to be invoked periodically or triggered by S3 events and then contain the logic to start the pipeline, whereas EventBridge can directly connect S3 events to SageMaker pipelines. Amazon MWAA (Managed Workflows for Apache Airflow) is a powerful orchestration service, but it introduces more operational overhead and complexity than necessary for this specific task of reacting to S3 events to trigger a SageMaker pipeline. EventBridge offers a simpler, serverless, and more direct integration.