MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A team wants to use AWS Step Functions to orchestrate a retraining workflow that is triggered when new data arrives in an S3 bucket. They also need to monitor model drift. Which event-driven approach should they use?
⚠ Common exam trap
Many candidates confuse S3 event notifications (which directly invoke Lambda) with EventBridge (which can target Step Functions), and they overlook that Step Functions is the recommended orchestration service for complex ML workflows, not just Lambda or SageMaker Pipelines alone.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure EventBridge to capture S3 PutObject events and target an AWS Step Functions state machine that runs the retraining pipeline
AWS EventBridge can capture S3 PutObject events (via S3's default event notifications or a more granular EventBridge rule) and directly target a Step Functions state machine as a target. This creates a fully event-driven, serverless orchestration for the retraining pipeline without polling or custom code. Step Functions then coordinates the retraining steps, including model drift monitoring, in a reliable and auditable manner.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure EventBridge to capture S3 PutObject events and target an AWS Step Functions state machine that runs the retraining pipeline
Why this is correct
EventBridge triggers the Step Functions workflow upon new data arrival, allowing orchestration of retraining and drift monitoring.
- ✗
Use a cron-based Step Function schedule that checks for new data every hour
Why it's wrong here
Scheduled checks are less efficient than event-driven; they also introduce latency.
- ✗
Set up an S3 event notification to invoke a Lambda function that starts a SageMaker training job directly
Why it's wrong here
While possible, this lacks orchestration capabilities; Step Functions provides better workflow management and error handling.
- ✗
Use SageMaker Pipelines with a schedule trigger
Why it's wrong here
SageMaker Pipelines can be scheduled, but not directly triggered by S3 events without EventBridge.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.