MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A team uses MLflow on SageMaker for experiment tracking. They want to automate the retraining of a model when new training data arrives in an S3 bucket. Which combination of services should they use?
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
✓
EventBridge -> Lambda -> SageMaker Training Job
EventBridge can detect S3 PutObject events and trigger a Lambda function that starts a SageMaker training job, possibly using MLflow for tracking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Pipelines scheduled trigger every hour
Why it's wrong here
Scheduled trigger doesn't respond to new data arrival immediately.
- ✓
EventBridge -> Lambda -> SageMaker Training Job
Why this is correct
EventBridge captures S3 events, Lambda initiates training, and MLflow can log the run.
- ✗
S3 Event Notifications -> SQS -> SageMaker Training Job
Why it's wrong here
SQS can queue messages but needs a consumer (like Lambda) to trigger the training job; it's not direct.
- ✗
AWS Step Functions with S3 poller
Why it's wrong here
Polling is inefficient and not as event-driven as 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 |
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