MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning team needs to deploy a new model version for A/B testing, gradually shifting traffic from the old version to the new version over 24 hours. Which deployment strategy should they use?
⚠ Common exam trap
AWS often tests the distinction between canary and blue/green deployment, where candidates mistakenly choose blue/green because both involve two versions, but blue/green is an instant switch, not a gradual traffic shift.
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
Canary deployment is the correct strategy because it allows gradual traffic shifting from the old model version to the new one over a specified time period (e.g., 24 hours) while monitoring for errors or performance degradation. This approach minimizes risk by exposing only a small percentage of users to the new version initially, then incrementally increasing traffic as confidence grows, which aligns perfectly with the A/B testing requirement.
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 deployment is incorrect because it performs an instant cutover of all traffic from the old to the new version, rather than supporting the gradual traffic shifting required for A/B testing over 24 hours. This strategy is tempting as it offers zero-downtime deployments and rapid rollback capabilities. It would be the correct choice for scenarios demanding an immediate, full-scale switch to a new model version, where a phased rollout is not required.
- ✗
Shadow testing
Why it's wrong here
Shadow testing mirrors live requests to the new model without returning its predictions to users, so no traffic is actually shifted and outcomes cannot be compared. It suits safe pre-production validation of latency and accuracy, not the gradual user-facing rollout this scenario requires.
- ✗
Direct deployment with immediate full traffic
Why it's wrong here
Direct full-traffic deployment switches every request to the new version at once, so no gradual percentage-based traffic split over 24 hours occurs and rollback risk is unmanaged. It is tempting when a model is fully validated and instant cutover is acceptable, but A/B testing requires incremental traffic shifting.
- ✓
Canary deployment
Why this is correct
Canary deployment routes a small percentage of live traffic to the new model version first, then incrementally increases that share as metrics stay healthy. This directly satisfies the stem's requirement to shift traffic gradually over 24 hours while limiting blast radius if the new version underperforms.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.