- A
Blue/green deployment
Why wrong: Blue/green deployment switches all traffic at once, not gradually.
- B
Canary deployment
Canary deployment gradually shifts traffic, starting with a small percentage like 5%, to test the new version.
- C
Shadow testing
Why wrong: Shadow testing sends duplicate traffic to both versions but the new version's responses are not used; it is not a gradual rollout.
- D
Rolling deployment
Why wrong: Rolling deployment is not a standard SageMaker term; canary is the correct term for gradual traffic shift.
Question 733 of 1,000
Canary Deployment on SageMaker for Gradual Model Rollout
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 switches all traffic at once, not gradually.
- ✓
Canary deployment
Why this is correct
Canary deployment gradually shifts traffic, starting with a small percentage like 5%, to test the new version.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Shadow testing
Why it's wrong here
Shadow testing sends duplicate traffic to both versions but the new version's responses are not used; it is not a gradual rollout.
- ✗
Rolling deployment
Why it's wrong here
Rolling deployment is not a standard SageMaker term; canary is the correct term for gradual traffic shift.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
In canary deployments, a load balancer or service mesh (e.g., AWS App Mesh, Istio) is configured to route a specified percentage of requests to the canary version using weighted routing, often implemented via header-based or weight-based rules. The canary version runs alongside the stable version, and metrics such as error rate, latency, and prediction drift are monitored; if the canary exceeds a threshold (e.g., 1% error rate increase), traffic is automatically reverted. This pattern is critical for high-stakes ML models where a regression could cause financial or safety impacts, such as fraud detection or autonomous driving.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Deployment and Orchestration of ML Workflows — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, 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 uses production variants with weighted traffic allocation. By setting the new model variant to 5% and the current to 95%, and later adjusting weights, the team can gradually shift traffic. Blue/green is a full switch, and shadow testing duplicates traffic without affecting live responses.
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Last reviewed: Jul 4, 2026
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