Generative AI Leader Fundamentals of Generative AI Practice Question
A developer is deploying a generative AI model on Vertex AI for a production application that requires low latency and high throughput. They need to choose an appropriate endpoint type. Which deployment option should they use?
⚠ Common exam trap
Watch out — candidates often confuse model deployment endpoints with other Vertex AI services that manage or monitor models but do not serve predictions.
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
✓
Online prediction with a dedicated endpoint
Online prediction with a dedicated endpoint is the correct choice for low-latency, high-throughput serving. It allows the application to send requests and receive immediate responses, with autoscaling to handle varying loads. Batch prediction, Pipelines, and monitoring serve different purposes and cannot fulfill the real-time inference requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Online prediction with a dedicated endpoint
Why this is correct
Online prediction with a dedicated endpoint provides synchronous, low-latency responses and can scale to handle high throughput. It is the standard deployment option for real-time applications. Dedicated endpoints allow for consistent performance and can be configured with autoscaling to meet demand.
- ✗
Vertex AI Pipelines
Why it's wrong here
Vertex AI Pipelines is a workflow orchestration service for ML tasks, not a model serving endpoint. It is used to automate training and deployment steps, but it does not provide the inference API itself. It cannot serve live prediction requests.
- ✗
Model monitoring
Why it's wrong here
Model monitoring is a feature for detecting drift and anomalies in deployed models. It does not serve predictions; it observes them. Enabling it does not provide an endpoint for the application to call. It is a complementary tool for maintaining model quality.
- ✗
Batch prediction
Why it's wrong here
Batch prediction is designed for asynchronous processing of large datasets, not for real-time, low-latency responses. It would introduce significant delays and is unsuitable for interactive applications. Batch prediction is cost-effective for bulk jobs but not for serving live traffic.
Go deeper
Related to this question
About these practice questions
One of 1,008 original Generative AI Leader practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This Generative AI Leader 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 Generative AI Leader exam.