- A
Vertex AI Pipeline
Why wrong: Pipelines orchestrate ML workflows, not serving.
- B
Vertex AI Model Registry
Why wrong: Model Registry stores and versions models, but does not provide an endpoint.
- C
Vertex AI Feature Store
Why wrong: Feature Store is for managing and serving features, not for deploying models.
- D
Vertex AI Endpoint
Correct. An endpoint is required to deploy a model and obtain a URL for online predictions.
Question 602 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 engineer wants to deploy a trained model to Vertex AI for online predictions. Which Vertex AI resource is required to serve the model and provide an endpoint URL?
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
✓
Vertex AI Endpoint
Vertex AI Endpoint is the required resource to deploy a trained model for online predictions, as it provides a dedicated endpoint URL that accepts prediction requests and routes them to the model. Without an endpoint, the model cannot be accessed via HTTP/HTTPS for real-time inference, which is the core requirement for online serving.
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.
- ✗
Vertex AI Pipeline
Why it's wrong here
Pipelines orchestrate ML workflows, not serving.
- ✗
Vertex AI Model Registry
Why it's wrong here
Model Registry stores and versions models, but does not provide an endpoint.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store is for managing and serving features, not for deploying models.
- ✓
Vertex AI Endpoint
Why this is correct
Correct. An endpoint is required to deploy a model and obtain a URL for online predictions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the Model Registry (which stores and versions models) with the actual serving infrastructure, assuming that registering a model automatically creates an endpoint, when in fact a separate Endpoint resource must be created and the model must be deployed to it.
Detailed technical explanation
How to think about this question
Under the hood, a Vertex AI Endpoint uses a gRPC or REST API to serve predictions, with automatic scaling based on traffic and support for A/B testing via traffic splitting between model versions. In a real-world scenario, you might deploy multiple model versions to the same endpoint and gradually shift traffic from an old version to a new one using the endpoint's traffic split configuration, which is critical for safe rollouts.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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
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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: Vertex AI Endpoint — Vertex AI Endpoint is the required resource to deploy a trained model for online predictions, as it provides a dedicated endpoint URL that accepts prediction requests and routes them to the model. Without an endpoint, the model cannot be accessed via HTTP/HTTPS for real-time inference, which is the core requirement for online serving.
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.
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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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