The correct answer is to configure the endpoint with a traffic split of 95% to the old version and 5% to the new version. This works because Vertex AI endpoints natively support traffic splitting between model versions, allowing you to route a precise percentage of inference requests to each deployed model without managing separate infrastructure. On the Google Professional Data Engineer exam, this tests your understanding of MLOps deployment strategies and the specific configuration options within Vertex AI—a common trap is confusing a canary deployment with creating a completely separate endpoint for testing, which defeats the purpose of gradual, controlled rollout. Remember that traffic split is a configuration property of a single endpoint, not a separate deployment. A useful memory tip: think of it as a "95/5 faucet" where you simply adjust the knob to control the flow between old and new versions, keeping the pipeline unified.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. 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 machine learning team wants to deploy a new model version for canary testing, where only 5% of traffic is routed to the new version. Which Vertex AI endpoint configuration supports this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure the endpoint with traffic split: 95% to old version, 5% to new version.
Vertex AI supports traffic splitting between model versions in endpoints. Option B is correct. Option A is wrong because it deploys a separate endpoint. Option C is wrong because it suggests direct traffic control by client. Option D is wrong because A/B testing is a process, not configuration.
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.
✗
Have the client application randomly select which model to call with 5% probability.
Why it's wrong here
This puts the logic on the client side and is not manageable at the server level.
✗
Deploy the new version to a separate endpoint and direct 5% of users via a load balancer.
Why it's wrong here
This adds complexity and does not leverage Vertex AI's built-in traffic splitting.
✓
Configure the endpoint with traffic split: 95% to old version, 5% to new version.
Why this is correct
Vertex AI endpoints allow splitting traffic between deployed models; the platform handles routing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use an A/B testing framework outside of Vertex AI to compare results.
Why it's wrong here
This is a process, not a deployment configuration for traffic routing.
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 PDE 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.
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure the endpoint with traffic split: 95% to old version, 5% to new version. — Vertex AI supports traffic splitting between model versions in endpoints. Option B is correct. Option A is wrong because it deploys a separate endpoint. Option C is wrong because it suggests direct traffic control by client. Option D is wrong because A/B testing is a process, not configuration.
What should I do if I get this PDE question wrong?
Identify which PDE 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 →
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. A company deploys a model to Vertex AI Endpoint. They want to run a canary deployment to test a new model version with 10% of traffic. How should they configure this?
medium
A.Deploy to a new endpoint and update the application to call both
B.Use Cloud Load Balancing to route traffic
✓ C.Deploy the new model to the same endpoint and set traffic split
D.Deploy to Cloud Run and use gradual rollout
Why C: Option C is correct because Vertex AI Endpoints natively support traffic splitting between model versions deployed to the same endpoint. By deploying the new model version to the same endpoint and setting a traffic split of 10% to the new version and 90% to the current version, the company can perform a canary deployment without changing the application code or infrastructure.
Last reviewed: Jun 24, 2026
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This PDE 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 PDE exam.
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