The answer is the gcloud ai endpoints update command with --traffic-split=model_v1=1,model_v2=0. This is correct because traffic splitting on Vertex AI endpoints allows you to instantly reallocate inference requests between model versions without redeploying or deleting any models, enabling immediate rollback by setting model_v1 to 100% and model_v2 to 0%. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of canary deployments and safe rollback strategies—a common trap is trying to delete the faulty model version, which causes endpoint downtime, or using a misspelled command like “traffic-split.” Remember that Vertex AI endpoints treat traffic splits as a live configuration: you can shift traffic in real time without disrupting service. A useful memory tip is “split to zero, not delete the hero”—always adjust the traffic split to zero for the bad version rather than removing it, keeping the endpoint stable.
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.
Refer to the exhibit. A data scientist deploys a new model version (model_v2) to an existing endpoint with 20% traffic. After a few days, they notice that model_v2's error rate is higher than model_v1's. They want to route all traffic back to model_v1 immediately. Which command achieves this with minimal disruption?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "which command"
Why it matters: Tests specific CLI syntax. Recall the exact command and its required context — near-synonyms and partial matches are common distractors.
Clue: "immediately / without restart"
Why it matters: Time or reboot constraint — the correct answer must take effect right away without requiring a reboot or reload.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
gcloud ai endpoints update my-endpoint --region=us-central1 --traffic-split=model_v1=1,model_v2=0
Option A is correct. The gcloud ai endpoints update command with --traffic-split allows setting the traffic split to 100% for model_v1 and 0% for model_v2, routing all traffic to the stable model without redeploying. Option B removes the model, which may cause temporary unavailability. Option C uses a misspelled command. Option D changes the endpoint's update time but not traffic.
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.
✗
gcloud ai endpoints update my-endpoint --region=us-central1 --remove-deployed-model=model_v2
Why it's wrong here
Removing the deployed model may cause errors if the removal happens before traffic is redirected.
✗
gcloud ai endpoints undepoly-model my-endpoint --region=us-central1 --model=model_v2
Why it's wrong here
The command is misspelled ('undepoly') and does not exist.
✓
gcloud ai endpoints update my-endpoint --region=us-central1 --traffic-split=model_v1=1,model_v2=0
Why this is correct
This command updates the traffic split to direct 100% traffic to model_v1 and 0% to model_v2, a zero-downtime change.
Clue confirmation
The clue words "which command", "immediately / without restart" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
gcloud ai endpoints update-traffic my-endpoint --region=us-central1 --model=model_v1 --traffic-percentage=100
Why it's wrong here
The 'update-traffic' command does not exist; the correct syntax uses '--traffic-split'.
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.
Trap categories for this question
Command / output trap
The command is misspelled ('undepoly') and does not exist.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
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: gcloud ai endpoints update my-endpoint --region=us-central1 --traffic-split=model_v1=1,model_v2=0 — Option A is correct. The gcloud ai endpoints update command with --traffic-split allows setting the traffic split to 100% for model_v1 and 0% for model_v2, routing all traffic to the stable model without redeploying. Option B removes the model, which may cause temporary unavailability. Option C uses a misspelled command. Option D changes the endpoint's update time but not traffic.
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.
Are there clue words in this question I should notice?
Yes — watch for: "which command", "immediately / without restart". Tests specific CLI syntax. Recall the exact command and its required context — near-synonyms and partial matches are common distractors.
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 team uses Vertex AI Prediction with a custom container. They want to perform canary deployments by sending 5% of traffic to a new model version. Which method should they use?
medium
A.Create a new endpoint with manual traffic splitting
✓ B.Deploy two separate endpoints and use a load balancer
C.Use Cloud Run for serving with gradual rollout
D.Use the Vertex AI Model Registry and configure traffic splitting on the endpoint
Why B: Option C is correct because Vertex AI endpoints support traffic splitting between deployed models, allowing a controlled canary rollout. Option A is not possible as endpoints cannot have separate traffic splits on different deployments without manual configuration. Option B is incorrect as Model Registry itself does not handle traffic splitting. Option D uses Cloud Run which is not integrated with Vertex AI Prediction.
Last reviewed: Jun 24, 2026
Question Discussion
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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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