Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A software vendor is building a product that must call a Gemini model through a stable, versioned API with enterprise controls such as VPC Service Controls, and must run on Google Cloud infrastructure. Which Google Cloud offering should the vendor use?
⚠ Common exam trap
A common mix-up: candidates confuse the convenient prototyping API with the enterprise API; only the Vertex AI endpoint provides the versioning and Google Cloud governance controls a shipped product requires.
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
✓
The Gemini API in Vertex AI
The Gemini API in Vertex AI is the enterprise path to Google's foundation models: it offers versioned endpoints, runs on Google Cloud, and integrates with IAM, VPC Service Controls, and audit logging. Workspace is an end-user assistant, AI Studio targets prototyping without enterprise governance, and Feature Store serves ML features rather than generative model inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Gemini for Google Workspace
Why it's wrong here
Gemini for Google Workspace is an end-user assistant embedded in productivity apps, not a programmable API for building third-party products. It does not provide the versioned model endpoint or the VPC Service Controls integration the vendor needs, so it cannot serve as the integration point for their application.
- ✗
Google AI Studio
Why it's wrong here
Google AI Studio is a developer playground for rapid prototyping with Gemini models, and its API is aimed at experimentation rather than enterprise-governed production workloads. It lacks the VPC Service Controls and organization-level governance the vendor requires, making it unsuitable for a shipped product with strict controls.
- ✓
The Gemini API in Vertex AI
Why this is correct
The Gemini API in Vertex AI exposes Google's foundation models through a versioned enterprise endpoint that supports Google Cloud controls including VPC Service Controls, IAM, and audit logging. It runs on Google Cloud infrastructure and is designed for building production applications, so it satisfies the stability and governance requirements described.
- ✗
Vertex AI Feature Store
Why it's wrong here
Vertex AI Feature Store manages and serves machine learning features for training and prediction, which is unrelated to calling a generative model through an API. It provides no Gemini endpoint, versioning, or generative AI governance controls, so it cannot fulfill this integration requirement.
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.