- A
Use Vertex AI Experiments to track the traffic split percentages.
Why wrong: Experiments track training runs, not endpoint traffic splits.
- B
Enable Cloud Memorystore to cache identical requests for both models.
Why wrong: Caching is irrelevant for A/B testing traffic splitting.
- C
Deploy the challenger model to the same endpoint as the champion with a separate deployed model.
Multiple models can be deployed on one endpoint with traffic allocation.
- D
Configure traffic split in the endpoint's traffic_split field (e.g., champion:90, challenger:10).
Traffic split is set on the endpoint to distribute requests between deployed models.
- E
Use Cloud Monitoring to track custom metrics like conversion rate per model version.
Why wrong: Monitoring is necessary but not a step in traffic splitting; it's a separate activity.
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 retail company deploys a new recommendation model alongside the current champion on Vertex AI Endpoints. They want to gradually shift traffic to the challenger while monitoring business metrics (conversion rate). Which two steps are required? (Choose 2)
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 challenger model to the same endpoint as the champion with a separate deployed model.
Option C is correct because Vertex AI Endpoints support deploying multiple model versions (champion and challenger) to the same endpoint, each as a separate deployed model. This allows the endpoint to serve both models simultaneously, enabling traffic splitting without requiring separate endpoints or infrastructure.
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.
- ✗
Use Vertex AI Experiments to track the traffic split percentages.
Why it's wrong here
Experiments track training runs, not endpoint traffic splits.
- ✗
Enable Cloud Memorystore to cache identical requests for both models.
Why it's wrong here
Caching is irrelevant for A/B testing traffic splitting.
- ✓
Deploy the challenger model to the same endpoint as the champion with a separate deployed model.
Why this is correct
Multiple models can be deployed on one endpoint with traffic allocation.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Configure traffic split in the endpoint's traffic_split field (e.g., champion:90, challenger:10).
Why this is correct
Traffic split is set on the endpoint to distribute requests between deployed models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Monitoring to track custom metrics like conversion rate per model version.
Why it's wrong here
Monitoring is necessary but not a step in traffic splitting; it's a separate activity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
This question tests the distinction between monitoring (which is optional after deployment) and the actual configuration steps required to shift traffic; candidates mistakenly select Cloud Monitoring (Option E) as a required step, but the question specifically asks for steps to 'gradually shift traffic,' which is accomplished by deploying to the same endpoint and setting the traffic split, not by monitoring after the fact.
Detailed technical explanation
How to think about this question
The traffic_split field in Vertex AI Endpoints uses a percentage-based allocation (e.g., champion:90, challenger:10) that directs incoming requests to the corresponding deployed model; this is implemented via a weighted random selection at the endpoint's load balancer. Under the hood, Vertex AI assigns each deployed model a unique ID, and the traffic split is enforced by the endpoint's routing layer, which can be updated without redeploying models, enabling seamless gradual rollouts. In real-world scenarios, teams often start with a 99:1 split to validate the challenger's performance before ramping up, using the same endpoint to minimize latency and operational overhead.
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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Serving and Scaling Models — study guide chapter
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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 challenger model to the same endpoint as the champion with a separate deployed model. — Option C is correct because Vertex AI Endpoints support deploying multiple model versions (champion and challenger) to the same endpoint, each as a separate deployed model. This allows the endpoint to serve both models simultaneously, enabling traffic splitting without requiring separate endpoints or infrastructure.
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.
About these practice questions
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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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