Free AWS MLA-C01 Actual Exam Questions - Question 15 Discussion
delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to
implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?
Makes sense to skip polling since it's inefficient. C should be the easiest — EventBridge triggers right on the upload event, so no extra Lambda or Airflow setup needed. Definitely less to maintain overall. C
Probably C here. EventBridge can listen for S3 object-created events and trigger the SageMaker pipeline directly, so you avoid any extra Lambda functions or complicated orchestration setups. That’s less work to manage over time. A and D sound like overkill since Lifecycle rules aren’t meant to trigger training and MWAA is more for complex workflows, not simple event triggers. B works but has more overhead with the Lambda polling or scanning, which isn’t necessary given EventBridge’s native integration with S3 events.
C imo, EventBridge handles this natively without extra code or services.
B/C? Lambda scanning the bucket regularly sounds inefficient and adds maintenance, so probably not B. EventBridge can catch S3 object created events if configured properly, and it can trigger SageMaker pipelines directly, so C seems cleaner. D with MWAA is overkill and adds complexity for just triggering on uploads. A lifecycle rule is about managing storage, not triggering processes, so A doesn’t fit here. So, C looks like the least operational overhead once set up correctly.
C looks like the easiest way to trigger the pipeline automatically on S3 upload events without extra scanning or complex workflows.