MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company uses Amazon SageMaker to deploy a model for real-time predictions. The model is updated weekly. The company wants to ensure that the new model version is gradually rolled out to a small percentage of traffic before full deployment, and that it can be rolled back quickly if issues are detected. Which deployment strategy should be used?
⚠ Common exam trap
Test-takers frequently confuse A/B testing (a statistical evaluation method) with canary deployment (a traffic management strategy), leading them to select Option B even though SageMaker's endpoint variants directly support gradual traffic shifting and rollback.
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
✓
Canary deployment using SageMaker endpoint variants
Amazon SageMaker endpoint variants support canary deployments, where you can shift a small percentage of traffic to a new model version (e.g., 5%) while the majority remains on the old version. This allows gradual rollout and immediate rollback by simply adjusting the traffic distribution weights or deleting the new variant, meeting the requirement for quick rollback without redeploying.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Blue/green deployment
Why it's wrong here
Blue/green switches all traffic at once, which does not allow gradual rollout.
- ✗
A/B testing with a holdout group
Why it's wrong here
A/B testing is for evaluation, not for gradual rollout in production.
- ✓
Canary deployment using SageMaker endpoint variants
Why this is correct
Canary deployment allows sending a small percentage of traffic to the new variant and can be rolled back by shifting traffic back.
- ✗
Rolling deployment across multiple endpoints
Why it's wrong here
SageMaker does not natively support rolling deployments across endpoints.
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