- A
Use Cloud Load Balancing with weighted backend services pointing to different endpoints.
Why wrong: Not necessary; Vertex AI endpoints handle traffic splitting internally.
- B
Deploy the challenger to the same endpoint with initial traffic split, e.g., champion 90%, challenger 10%, and gradually adjust.
This is the correct method for A/B testing with traffic splitting in Vertex AI.
- C
Delete the champion model and redeploy with the challenger as the new version.
Why wrong: This would cause downtime and does not allow gradual traffic shift.
- D
Create a new endpoint for the challenger and use a load balancer to split traffic.
Why wrong: This adds unnecessary complexity; Vertex AI endpoints support multiple deployed models with traffic splitting.
Question 541 of 1,000
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.
You are deploying a new version of a model to a Vertex AI endpoint that already has a champion model serving 100% of traffic. You want to gradually shift traffic to the new version while monitoring for errors. Which approach should you 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
✓
Deploy the challenger to the same endpoint with initial traffic split, e.g., champion 90%, challenger 10%, and gradually adjust.
Vertex AI endpoints support traffic splitting between model versions deployed to the same endpoint. By deploying the challenger to the same endpoint and setting an initial split (e.g., champion 90%, challenger 10%), you can gradually shift traffic while monitoring for errors. This approach uses the endpoint's built-in traffic management, avoiding the complexity and latency of external load balancers.
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 Cloud Load Balancing with weighted backend services pointing to different endpoints.
Why it's wrong here
Not necessary; Vertex AI endpoints handle traffic splitting internally.
- ✓
Deploy the challenger to the same endpoint with initial traffic split, e.g., champion 90%, challenger 10%, and gradually adjust.
Why this is correct
This is the correct method for A/B testing with traffic splitting in Vertex AI.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Delete the champion model and redeploy with the challenger as the new version.
Why it's wrong here
This would cause downtime and does not allow gradual traffic shift.
- ✗
Create a new endpoint for the challenger and use a load balancer to split traffic.
Why it's wrong here
This adds unnecessary complexity; Vertex AI endpoints support multiple deployed models with traffic splitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that external load balancers are required for traffic splitting, when in fact Vertex AI endpoints provide native traffic management that is simpler and more appropriate for model versioning.
Detailed technical explanation
How to think about this question
Vertex AI endpoints use a traffic split configuration that directs a percentage of inference requests to each model version based on a weighted random selection at the endpoint level. This is implemented via the `traffic_split` parameter in the `DeployModel` API, where you specify a dictionary mapping model IDs to integer percentages summing to 100. The split is applied per request, not per session, ensuring granular control and enabling canary deployments with automatic rollback if error thresholds are exceeded.
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
- →
Serving and Scaling Models practice questions
Targeted practice on this topic area only
- →
All PMLE questions
1,000 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Automating and Orchestrating ML Pipelines practice questions
Practise PMLE questions linked to Automating and Orchestrating ML Pipelines.
Collaborating Within and Across Teams to Manage Data and Models practice questions
Practise PMLE questions linked to Collaborating Within and Across Teams to Manage Data and Models.
Serving and Scaling Models practice questions
Practise PMLE questions linked to Serving and Scaling Models.
Monitoring ML Solutions practice questions
Practise PMLE questions linked to Monitoring ML Solutions.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 to the same endpoint with initial traffic split, e.g., champion 90%, challenger 10%, and gradually adjust. — Vertex AI endpoints support traffic splitting between model versions deployed to the same endpoint. By deploying the challenger to the same endpoint and setting an initial split (e.g., champion 90%, challenger 10%), you can gradually shift traffic while monitoring for errors. This approach uses the endpoint's built-in traffic management, avoiding the complexity and latency of external load balancers.
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
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 →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.