MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A team needs to deploy a new model version to production while minimizing risk. They want to route 5% of live traffic to the new model and 95% to the current model, and then gradually increase the new model's traffic. Which SageMaker deployment pattern should they use?
⚠ Common exam trap
The trap is confusing canary deployment with A/B testing; both use production variants, but canary is for gradual traffic shifting to minimize risk, while A/B testing is for comparing model performance with a fixed split.
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 production variants
Canary deployment using production variants in Amazon SageMaker allows you to route a small percentage of live traffic (e.g., 5%) to the new model version while the rest goes to the existing model. You can then gradually increase the traffic to the new model as confidence grows, minimizing risk. This pattern is specifically designed for progressive rollouts with the ability to roll back if issues arise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Shadow testing
Why it's wrong here
Shadow testing mirrors live traffic to the new model without returning its responses to users, so it cannot route 5% of real traffic to it. Shadow testing suits validating a model against production data before release, whereas gradual live traffic shifting requires a canary or linear deployment pattern.
- ✗
Blue/green deployment
Why it's wrong here
Blue/green swaps all traffic between two identical environments via a single cutover, so it cannot hold a 5%/95% split or incrementally shift percentages. It is tempting because it gives instant rollback by repointing the endpoint, which suits full-release cutovers where zero-downtime switching matters more than gradual exposure.
- ✗
A/B testing with production variants
Why it's wrong here
A/B testing with production variants compares variant performance for experimentation; it does not natively support incrementally ramping a variant's traffic share. It is tempting because it also uses multiple production variants, but its purpose is measuring metric differences between models, not staged rollout with progressive traffic shifting.
- ✓
Canary deployment using production variants
Why this is correct
Canary deployment using production variants lets you split live traffic across multiple model variants behind one endpoint, initially weighting 5% to the new version and 95% to the current one. You then shift the traffic distribution gradually, satisfying the low-risk, incremental rollout constraint.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.