- A
Create a new endpoint for the new version and update the client to call both endpoints.
Why wrong: This would require client-side changes and does not leverage Vertex AI's built-in traffic splitting.
- B
Deploy the new version and set the minimum replicas to 0, then gradually increase.
Why wrong: Min replicas control scaling, not traffic allocation. This does not split traffic.
- C
Use Cloud Load Balancing to distribute traffic between two endpoints.
Why wrong: Cloud Load Balancing is not designed for Vertex AI endpoint traffic splitting; Vertex AI has built-in traffic management.
- D
Deploy the new version as a separate model on the same endpoint and use the `traffic_split` parameter in the deployment request.
Correct: Vertex AI allows multiple deployed models on one endpoint with traffic split percentages.
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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 company is deploying a new model version to an existing Vertex AI endpoint. They want to test the new version with 5% of traffic before fully rolling it out. What is the correct approach?
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
Deploy the new version as a separate model on the same endpoint and use the `traffic_split` parameter in the deployment request.
Option D is correct because Vertex AI endpoints support traffic splitting between multiple deployed models. By deploying the new model version to the same endpoint and setting `traffic_split` to 5% for the new version and 95% for the existing version, the endpoint automatically routes a corresponding proportion of inference requests to each model without any client-side changes.
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.
- ✗
Create a new endpoint for the new version and update the client to call both endpoints.
Why it's wrong here
This would require client-side changes and does not leverage Vertex AI's built-in traffic splitting.
- ✗
Deploy the new version and set the minimum replicas to 0, then gradually increase.
Why it's wrong here
Min replicas control scaling, not traffic allocation. This does not split traffic.
- ✗
Use Cloud Load Balancing to distribute traffic between two endpoints.
Why it's wrong here
Cloud Load Balancing is not designed for Vertex AI endpoint traffic splitting; Vertex AI has built-in traffic management.
- ✓
Deploy the new version as a separate model on the same endpoint and use the `traffic_split` parameter in the deployment request.
Why this is correct
Correct: Vertex AI allows multiple deployed models on one endpoint with traffic split percentages.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse traffic splitting with scaling or load balancing, assuming that adjusting replicas or using an external load balancer is required, when Vertex AI's native `traffic_split` is the simplest and correct method for canary deployments.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI uses the `traffic_split` parameter to assign a percentage of incoming prediction requests to each model deployed on the same endpoint. The split is applied at the endpoint's routing layer, which uses a weighted random selection per request. This allows canary deployments with zero client changes, and the split can be adjusted over time (e.g., 5% → 50% → 100%) by updating the endpoint's traffic configuration via the `projects.locations.endpoints.patch` API.
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.
- →
Serving and Scaling Models — study guide chapter
Learn the concepts, then practise the questions
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Deploy the new version as a separate model on the same endpoint and use the `traffic_split` parameter in the deployment request. — Option D is correct because Vertex AI endpoints support traffic splitting between multiple deployed models. By deploying the new model version to the same endpoint and setting `traffic_split` to 5% for the new version and 95% for the existing version, the endpoint automatically routes a corresponding proportion of inference requests to each model without any client-side changes.
What should I do if I get this PMLE 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.
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Last reviewed: Jul 4, 2026
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.
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