- A
Vertex AI Batch Prediction Job
Why wrong: Batch prediction is for offline, not online serving.
- B
Vertex AI Endpoint
An endpoint is required to deploy a model for online predictions.
- C
Vertex AI Feature Store
Why wrong: Feature Store is for feature management, not serving models.
- D
Vertex AI Model Registry
Why wrong: Model Registry stores versions but does not serve predictions directly.
Question 392 of 1,000
Deploy Models for Online Prediction with Vertex AI Endpoint
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?
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.
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 correct resource for deploying a trained model to serve online predictions. It provides a managed endpoint that exposes a REST API for real-time inference requests, which is exactly what the data scientist needs for online prediction.
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 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
Common exam trap: answer the scenario, not the keyword
Google often tests the distinction between batch prediction and online prediction, leading candidates to mistakenly choose Batch Prediction Job when the question explicitly asks for 'online prediction' or 'real-time serving'.
Detailed technical explanation
How to think about this question
Under the hood, a Vertex AI Endpoint uses a load-balanced cluster of compute instances (e.g., n1-standard-2 VMs) running the model container, with autoscaling based on request traffic. The endpoint exposes a single gRPC or REST API endpoint, and you can deploy multiple models (or model versions) to the same endpoint for A/B testing or canary deployments by setting traffic splits. A real-world scenario is deploying a fraud detection model that must respond in under 100ms per request, which requires a low-latency online endpoint rather than a batch job.
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 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
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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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning 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 correct resource for deploying a trained model to serve online predictions. It provides a managed endpoint that exposes a REST API for real-time inference requests, which is exactly what the data scientist needs for online prediction.
What should I do if I get this PDE 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
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