- A
Canary deployments are only supported for custom containers, not prebuilt frameworks.
Why wrong: Supported for all container types.
- B
You can roll back a canary by resetting traffic to 0% for the new version.
Traffic can be shifted back to old version easily.
- C
You can use traffic splitting to gradually shift 1-100% of traffic to a new version.
Vertex AI allows splitting traffic between model versions.
- D
Canary deployments require the use of Vertex AI Model Registry.
Why wrong: Model Registry is not mandatory.
- E
Once a canary receives 50% traffic, you cannot increase it further.
Why wrong: You can increase to 100%.
Question 994 of 1,000
hardMultiple SelectObjective-mapped
Canary Deployments on Vertex AI Endpoints
This PMLE practice question tests your understanding of pmle exam topics. 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.
Which TWO statements are true about canary deployments for Vertex AI endpoints?
Quick Answer
The answer is that you can use traffic splitting to gradually shift 1-100% of traffic to a new version, and that canary deployments help test a model before a full rollout. This is correct because Vertex AI endpoints support traffic splitting between model versions, allowing you to route a small percentage of live traffic to a new model while monitoring its performance, then incrementally increase that percentage up to 100% without exceeding a maximum traffic limit per split adjustment. On the Google Professional Machine Learning Engineer exam, this tests your understanding of safe deployment strategies and automated rollback triggers using monitoring metrics, with a common trap being the misconception that you can instantly jump to 100% traffic or that traffic splitting is only for A/B testing. Remember the memory tip: “Split, test, shift—canary gives you the lift.”
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
✓
You can roll back a canary by resetting traffic to 0% for the new version.
Traffic splitting in Vertex AI endpoints allows you to gradually shift traffic from 1% to 100% to a new version. You can adjust the percentage at any time, including increasing it up to 100% for full rollout. To roll back, set the new version's traffic to 0%, sending all traffic to the old version. Canary deployments help test a new version with a small traffic percentage before full rollout, and automated rollback can be triggered based on monitoring metrics.
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.
- ✗
Canary deployments are only supported for custom containers, not prebuilt frameworks.
Why it's wrong here
Supported for all container types.
- ✓
You can roll back a canary by resetting traffic to 0% for the new version.
Why this is correct
Traffic can be shifted back to old version easily.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
You can use traffic splitting to gradually shift 1-100% of traffic to a new version.
Why this is correct
Vertex AI allows splitting traffic between model versions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Canary deployments require the use of Vertex AI Model Registry.
Why it's wrong here
Model Registry is not mandatory.
- ✗
Once a canary receives 50% traffic, you cannot increase it further.
Why it's wrong here
You can increase to 100%.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: You can roll back a canary by resetting traffic to 0% for the new version. — Traffic splitting in Vertex AI endpoints allows you to gradually shift traffic from 1% to 100% to a new version. You can adjust the percentage at any time, including increasing it up to 100% for full rollout. To roll back, set the new version's traffic to 0%, sending all traffic to the old version. Canary deployments help test a new version with a small traffic percentage before full rollout, and automated rollback can be triggered based on monitoring metrics.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 PMLE
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. You run the above command to deploy a new model version to an existing endpoint. After deployment, you observe that the endpoint's previous model version is still receiving 100% of traffic. What is the most likely reason for this?
medium- A.The new model is still in the 'creating' state and hasn't been activated.
- B.The model ID provided does not exist in the endpoint.
- ✓ C.The --traffic-split flag is specified incorrectly; it should use model IDs, not '0-100'.
- D.The min-replica-count is too high, preventing traffic splitting.
Why C: The traffic-split flag syntax is incorrect. The correct syntax for Vertex AI is --traffic-split=<model-id>=<percentage> for each model. Without correct model IDs, the flag is ignored, and no traffic split is applied, so the existing version continues to receive all traffic.
Variation 2. After deploying a new version of a model to a Vertex AI Endpoint, the team notices that predictions are still returning results from the old version. The deployment command used a traffic split of 100% to the new version. What is the most likely cause?
medium- A.The model artifact uploaded was identical to the old version.
- ✓ B.The traffic split was not properly updated; the endpoint is still routing 100% to the old version.
- C.The new model version failed health checks and was automatically rolled back.
- D.The prediction client is caching the old model response.
Why B: The most likely cause is that the traffic split was not properly updated. Although the deployment command specified 100% traffic to the new version, the actual traffic split may not have been applied correctly, so the endpoint continues routing all traffic to the old version. Option A is incorrect because an identical artifact would not cause predictions to come from the old version. Option C is incorrect because a health check failure would prevent the new version from serving, but the deployment would have failed, not silently roll back. Option D is incorrect because Vertex AI does not cache predictions on the client side.
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Last reviewed: Jun 24, 2026
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