Quick Answer
The answer is Vertex AI Endpoint, the correct resource for deploying a trained model to serve online predictions in real time. This is because a Vertex AI Endpoint provides a managed, scalable HTTP endpoint that hosts one or more model versions, handling incoming requests and returning predictions with low latency. In contrast, the Model Registry is simply a central repository for storing and versioning models, not for serving them, while Batch Prediction Job processes large volumes of data asynchronously and Feature Store manages feature data for training and serving. On the Google Professional Data Engineer exam, this question tests your understanding of the deployment workflow and the distinction between serving resources—a common trap is confusing the Model Registry with the endpoint itself. A useful memory tip is to think of the Endpoint as the “live server” that answers the door for each prediction request, whereas the Registry is just the “garage” where models are parked.
This PDE practice question tests your understanding of operationalizing machine learning 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.
A data scientist trains a TensorFlow model using Vertex AI Training and wants to deploy it for online prediction. Which Vertex AI resource should the data scientist use to create an endpoint for serving predictions?
Vertex AI Batch Prediction Job
Why wrong: Batch prediction is for offline, not online serving.
Vertex AI Endpoint
An endpoint is required to deploy a model for online predictions.
Vertex AI Feature Store
Why wrong: Feature Store is for feature management, not serving models.
Vertex AI Model Registry
Why wrong: Model Registry stores versions but does not serve predictions directly.
Answer choices
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
Option A is correct because Vertex AI Endpoint is the resource for serving online predictions. Option B (Model Registry) stores models but does not serve. Option C (Batch Prediction Job) is for batch predictions. Option D (Feature Store) is for managing features.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Vertex AI Batch Prediction Job
Why it's wrong here
Batch prediction is for offline, not online serving.
Vertex AI Endpoint
Why this is correct
An endpoint is required to deploy a model for online predictions.
Related concept
Read the scenario before looking for a memorised answer.
Vertex AI Feature Store
Why it's wrong here
Feature Store is for feature management, not serving models.
Vertex AI Model Registry
Why it's wrong here
Model Registry stores versions but does not serve predictions directly.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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.
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FAQ
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
The correct answer is: Vertex AI Endpoint — Option A is correct because Vertex AI Endpoint is the resource for serving online predictions. Option B (Model Registry) stores models but does not serve. Option C (Batch Prediction Job) is for batch predictions. Option D (Feature Store) is for managing features.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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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