Generative AI Leader Fundamentals of Generative AI Practice Question
A retail company wants to build an internal assistant that answers employee questions using the company's own HR policy documents. The team has no machine learning engineers and wants a managed Google Cloud approach that grounds responses in those documents without training a new foundation model. Which Google Cloud capability best fits this need?
⚠ Common exam trap
The trap here is assuming grounding requires fine-tuning a model, when retrieval-augmented grounding through a managed search service is the intended no-training approach.
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 Search with grounding on a data store built from the HR documents
Vertex AI Search provides managed retrieval over enterprise documents and grounds generative answers in that content, so employees receive responses based on current HR policy rather than the model's general knowledge. It requires no foundation-model training and minimal ML operations, matching the team's skills and timeline. The other choices either demand heavy ML work or address unrelated prediction tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploying an open-source model on a Compute Engine GPU VM and fine-tuning it weekly
Why it's wrong here
Running and fine-tuning a model on raw GPU VMs requires infrastructure management, tuning expertise, and ongoing operations that the team explicitly lacks. It also does not provide document grounding out of the box. While technically possible, it is not the managed, no-ML-engineer solution the scenario calls for and adds unnecessary operational burden.
- ✓
Vertex AI Search with grounding on a data store built from the HR documents
Why this is correct
Vertex AI Search lets teams index enterprise documents into a data store and then ground generative responses on retrieved passages. It is a managed service requiring no model training, and it directly addresses the requirement to answer from the company's own HR policies. This matches the no-ML-engineer constraint and the grounding goal precisely.
- ✗
Using BigQuery ML to run a logistic regression over HR ticket categories
Why it's wrong here
BigQuery ML is suited to tabular prediction tasks such as classification or forecasting, not to generative question answering grounded in unstructured policy text. A logistic regression cannot synthesize natural-language answers from documents. It solves a different problem entirely and would not deliver the conversational assistant the employees need.
- ✗
Training a custom foundation model from scratch on the HR documents
Why it's wrong here
Training a foundation model from scratch demands enormous compute, large curated datasets, and deep ML expertise. It is far beyond what an internal HR assistant needs and contradicts the constraint of having no machine learning engineers. The goal is grounding responses, not creating a new base model, so this approach is both impractical and misaligned.
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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.