easyMultiple Choice
PDE Practice Question: A data scientist trains a TensorFlow model using…
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?
⚠ Common exam trap
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'.
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.
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 Jobs run asynchronous scoring against a model over stored input data and write predictions to a destination; they create no endpoint and serve no real-time requests. Online prediction requires deploying the model to an endpoint.
- ✓
Vertex AI Endpoint
Why this is correct
A Vertex AI Endpoint provides the managed serving resource that hosts the trained model and exposes an online prediction API. Deploying the model to an endpoint satisfies the online prediction requirement, whereas training jobs, datasets or model registry entries do not serve live requests.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store centralises feature definition, storage and serving for training and inference; it holds feature values, not deployed models, and exposes no prediction endpoint. It is chosen when features must be shared consistently across models.
- ✗
Vertex AI Model Registry
Why it's wrong here
Vertex AI Model Registry stores and versions trained models but does not serve predictions; it lacks the endpoint and deployed-model resources that online inference requires. It is tempting because registering a model is a genuine prerequisite step, and it would be the right choice when the task is cataloguing, versioning, or promoting models rather than exposing them for real-time traffic.
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