MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company needs to deploy a new model version to a SageMaker real-time endpoint. They want to route 5% of traffic to the new version initially to monitor for errors before full rollout. Which deployment strategy should they use?
⚠ Common exam trap
Many candidates confuse canary deployment with shadow testing, mistakenly thinking shadow testing also routes live user traffic, when in fact shadow testing only duplicates traffic for validation without affecting the user experience.
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 with production variants
A canary deployment with production variants allows you to route a specific percentage of traffic (e.g., 5%) to the new model version by adjusting the `InitialVariantWeight` parameter in the production variant configuration. This enables gradual traffic shifting while monitoring errors, and you can later increase the weight to 100% for full rollout. SageMaker real-time endpoints support this natively by hosting multiple model variants behind the same endpoint.
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, not a gradual percentage.
- ✗
Shadow testing
Why it's wrong here
Shadow testing sends duplicate traffic to a shadow endpoint for comparison, but does not serve real traffic.
- ✓
Canary deployment with production variants
Why this is correct
Production variants allow traffic splitting; setting initial weight to 5% on the new variant achieves a canary.
- ✗
Multi-model endpoint
Why it's wrong here
MME hosts many models, but does not inherently provide traffic shifting for deployment.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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