PMLE Serving and Scaling Models Practice Question
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?
⚠ Common exam trap
Many 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.
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.
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
Vertex AI Pipelines orchestrates training and processing workflows as directed acyclic graphs; it produces models but does not host them or expose an endpoint URL. It is tempting because pipelines are genuinely part of the MLOps lifecycle, and would be correct if the question asked how to automate and schedule repeatable training runs.
- ✗
Vertex AI Model Registry
Why it's wrong here
The Model Registry catalogues and versions trained models; it neither hosts an artefact for serving nor exposes a prediction endpoint URL. It is tempting because registering a model is a genuine prerequisite step before deployment, and would be the right answer if the question asked where to track model versions and lineage.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store serves and shares engineered feature values for training and online inference; it does not host model artefacts or expose a prediction endpoint. It is tempting because online serving does consume features at low latency, and it would be correct if the question asked where to store and serve features consistently across training and prediction.
- ✓
Vertex AI Endpoint
Why this is correct
An Endpoint is the managed Vertex AI resource that hosts the deployed model and exposes a serving URL for online predictions. Deploying the model to an Endpoint allocates compute and returns the endpoint URL the engineer needs to send prediction requests.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.