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MLA-C01 Practice Question: A team is using SageMaker Pipelines to automate…
A team is using SageMaker Pipelines to automate retraining and deployment. They want to trigger the pipeline automatically when new training data is available in an S3 bucket. Which approach should they use?
⚠ Common exam trap
The trap here is that candidates may overcomplicate the solution by choosing Step Functions (Option D) for orchestration, not realizing that EventBridge provides a simpler, event-driven trigger without the need for polling or additional state machines.
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
✓
Create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events
Amazon EventBridge can directly capture S3 PutObject events and invoke a SageMaker Pipeline execution as a target. This provides a fully event-driven, serverless integration without polling or manual intervention, aligning with best practices for automating ML workflows when new data arrives.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events
Why this is correct
EventBridge can detect S3 events and start pipeline executions.
- ✗
Register the pipeline as a model package in SageMaker Model Registry
Why it's wrong here
Model registry does not trigger pipelines.
- ✗
Configure a cron job to run the pipeline every hour
Why it's wrong here
Cron is periodic, not event-driven.
- ✗
Use AWS Step Functions to poll the S3 bucket and start the pipeline when a new object appears
Why it's wrong here
Polling an S3 bucket with Step Functions introduces latency between object creation and pipeline trigger, whereas the requirement demands immediate, event-driven initiation. This approach is tempting because Step Functions excels at orchestrating workflows and can poll S3 for new objects in batch-processing scenarios where slight delays are acceptable, such as nightly data ingestion jobs. However, the stem specifies automatic triggering upon availability, which is best served by S3 Event Notifications directly invoking the pipeline.
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 |
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