MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning engineer needs to deploy a new version of a model gradually, initially sending 5% of traffic to the new version and 95% to the current version, while monitoring for errors. Which deployment pattern should they use?
⚠ Common exam trap
Candidates often confuse canary deployment with blue/green deployment, thinking both involve gradual traffic shifting. However, blue/green is an all-or-nothing switch between environments, while canary allows incremental percentage-based routing (e.g., 5%) with real-time monitoring and automated 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
Canary deployment is the correct pattern because it allows the ML engineer to route a small percentage of traffic (e.g., 5%) to the new model version while keeping the majority (95%) on the current version. This enables gradual rollout with real-time monitoring for errors, and if issues are detected, traffic can be instantly shifted back to the stable version.
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 shifts all traffic to the new environment after validation; it cannot split 5% and 95% simultaneously. It is tempting because it limits blast radius during release, but gradual percentage-based routing with error monitoring is canary deployment, which is what the scenario describes.
- ✓
Canary deployment
Why this is correct
A canary deployment shifts a small percentage of traffic, here 5%, to the new model version while the remainder stays on the current version, allowing error monitoring before full rollout. This matches the gradual, low-risk traffic-splitting requirement in the stem.
- ✗
Shadow testing
Why it's wrong here
Shadow testing mirrors production traffic to the new model without returning its responses to users, so no live traffic percentage is served by it. Canary deployment is the pattern that splits traffic, such as 5% to the new version, which is what the scenario requires.
- ✗
Rolling deployment
Why it's wrong here
Rolling deployment replaces instances in batches, so traffic shifts fully to the new version as each batch completes rather than holding a fixed 5/95 split. It is tempting because rolling deployment does limit blast radius, which suits releasing a version across a fleet without a defined traffic percentage.
Go deeper
Related to this question
About these practice questions
One of 665 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.Blue/green deployment
- B.Shadow testing
- C.Direct deployment with immediate full traffic
- ✓ D.Canary deployment
Why D: 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.
Variation 2. 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?
medium- A.Shadow testing
- B.Blue/green deployment
- C.A/B testing with production variants
- ✓ D.Canary deployment using production variants
Why D: 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.
JA
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.