Generative AI Leader Fundamentals of Generative AI Practice Question
A startup is building a customer support chatbot using Vertex AI and wants to ground responses in their product documentation to reduce hallucinations. Which approach should they use?
⚠ Common exam trap
Google Cloud often tests the misconception that fine-tuning is the best way to incorporate domain knowledge, but the trap here is that fine-tuning does not provide dynamic, verifiable grounding with citations, whereas Vertex AI Grounding with a custom data store does, making it the correct choice for reducing hallucinations in a retrieval-augmented generation use case.
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
✓
Enable Vertex AI Grounding with a custom enterprise data store containing the documentation.
Vertex AI Grounding with a custom enterprise data store is the correct approach because it allows the chatbot to retrieve and cite specific chunks from the product documentation in real time, directly reducing hallucinations by constraining responses to verified content. This method uses the underlying grounding service to query a vector-based data store (powered by Vertex AI Search) and append source references to the model's output, ensuring factual accuracy without retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Vertex AI Grounding with a custom enterprise data store containing the documentation.
Why this is correct
Grounding ties responses to specific documents, reducing hallucinations.
- ✗
Use the Codey API for text generation.
Why it's wrong here
Codey is for code generation, not grounding documentation.
- ✗
Use the base model without any grounding to maximize flexibility.
Why it's wrong here
Lack of grounding increases hallucination risk.
- ✗
Fine-tune the model on the documentation and deploy.
Why it's wrong here
Fine-tuning alone does not guarantee real-time grounding.
Go deeper
Related to this question
About these practice questions
One of 683 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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.