- A
Deploy both versions on the same endpoint and use the `traffic_split` parameter to allocate 90% to v1 and 10% to v2.
Vertex AI endpoints support traffic splitting between deployed models.
- B
Configure a global load balancer in front of two endpoints and set the weight.
Why wrong: This adds unnecessary complexity and latency.
- C
Create two separate endpoints, one for each version, and have the client randomly select the endpoint.
Why wrong: This requires client-side logic and is not recommended.
- D
Deploy v2 as a canary deployment and set the canary rollout to 10% in Cloud Deployment Manager.
Why wrong: Deployment Manager doesn't handle traffic splitting for model endpoints.
Question 546 of 1,000
mediumMultiple ChoiceObjective-mapped
PMLE Practice Question: A team wants to deploy two versions of a model…
This PMLE practice question tests your understanding of pmle exam topics. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 team wants to deploy two versions of a model (v1 and v2) on Vertex AI Endpoint to conduct an A/B test. They need to split traffic so that 10% of requests go to v2. Which configuration achieves this?
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 both versions on the same endpoint and use the `traffic_split` parameter to allocate 90% to v1 and 10% to v2.
Option A is correct because Vertex AI Endpoints allow distributing traffic between deployed models using the traffic_split parameter. Setting 90% to v1 and 10% to v2 achieves the A/B test traffic split. Option B is incorrect because a global load balancer in front of two endpoints adds unnecessary complexity and is not the native Vertex AI method. Option C is incorrect because the client randomly selecting endpoints introduces client-side logic and does not leverage Vertex AI's built-in traffic splitting. Option D is incorrect because Cloud Deployment Manager's canary rollout is for infrastructure deployment, not for model traffic splitting; Vertex AI provides traffic splitting for A/B testing.
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.
- ✓
Deploy both versions on the same endpoint and use the `traffic_split` parameter to allocate 90% to v1 and 10% to v2.
Why this is correct
Vertex AI endpoints support traffic splitting between deployed models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure a global load balancer in front of two endpoints and set the weight.
Why it's wrong here
This adds unnecessary complexity and latency.
- ✗
Create two separate endpoints, one for each version, and have the client randomly select the endpoint.
Why it's wrong here
This requires client-side logic and is not recommended.
- ✗
Deploy v2 as a canary deployment and set the canary rollout to 10% in Cloud Deployment Manager.
Why it's wrong here
Deployment Manager doesn't handle traffic splitting for model endpoints.
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: Deploy both versions on the same endpoint and use the `traffic_split` parameter to allocate 90% to v1 and 10% to v2. — Option A is correct because Vertex AI Endpoints allow distributing traffic between deployed models using the traffic_split parameter. Setting 90% to v1 and 10% to v2 achieves the A/B test traffic split. Option B is incorrect because a global load balancer in front of two endpoints adds unnecessary complexity and is not the native Vertex AI method. Option C is incorrect because the client randomly selecting endpoints introduces client-side logic and does not leverage Vertex AI's built-in traffic splitting. Option D is incorrect because Cloud Deployment Manager's canary rollout is for infrastructure deployment, not for model traffic splitting; Vertex AI provides traffic splitting for A/B testing.
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
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Last reviewed: Jun 24, 2026
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