- A
Use Cloud Run to serve each model as a separate service.
Why wrong: Cloud Run is not integrated with Vertex AI model serving.
- B
Use a single endpoint with multiple deployed models and traffic allocation.
Multi-model serving allows deploying several models on one endpoint with traffic splitting.
- C
Deploy each model to separate endpoints and use a load balancer.
Why wrong: Using separate endpoints increases costs; the goal is to reduce costs by sharing an endpoint.
- D
Use Vertex AI Matching Engine to serve models.
Why wrong: Matching Engine is for vector search, not model serving.
Question 861 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling 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 company is deploying multiple models on a single Vertex AI endpoint to reduce costs. Each model has different traffic patterns. Which configuration should they 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
✓
Use a single endpoint with multiple deployed models and traffic allocation.
Vertex AI endpoints support deploying multiple models behind a single endpoint with traffic splitting, allowing you to route different percentages of requests to each model based on their traffic patterns. This reduces infrastructure costs compared to separate endpoints, as the endpoint's underlying compute resources are shared. Traffic allocation can be adjusted dynamically to match changing model usage without redeploying.
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 Run to serve each model as a separate service.
Why it's wrong here
Cloud Run is not integrated with Vertex AI model serving.
- ✓
Use a single endpoint with multiple deployed models and traffic allocation.
Why this is correct
Multi-model serving allows deploying several models on one endpoint with traffic splitting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy each model to separate endpoints and use a load balancer.
Why it's wrong here
Using separate endpoints increases costs; the goal is to reduce costs by sharing an endpoint.
- ✗
Use Vertex AI Matching Engine to serve models.
Why it's wrong here
Matching Engine is for vector search, not model serving.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Vertex AI endpoints with generic load balancing (Option C) or think separate services (Option A) are needed for different models, missing the cost-saving capability of traffic splitting on a single endpoint.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI endpoints use a gRPC-based serving infrastructure that can host multiple model versions, each with its own deployment resource pool (machine type, min/max replicas). Traffic allocation is implemented via a weighted round-robin mechanism at the endpoint's ingress, allowing fine-grained control (e.g., 80% to model A, 20% to model B) without downtime. A real-world scenario is A/B testing a new model version: you can gradually shift traffic from 0% to 100% while monitoring performance, then retire the old model — all on one endpoint.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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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Serving and Scaling Models practice questions
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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: Use a single endpoint with multiple deployed models and traffic allocation. — Vertex AI endpoints support deploying multiple models behind a single endpoint with traffic splitting, allowing you to route different percentages of requests to each model based on their traffic patterns. This reduces infrastructure costs compared to separate endpoints, as the endpoint's underlying compute resources are shared. Traffic allocation can be adjusted dynamically to match changing model usage without redeploying.
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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