Generative AI Leader Fundamentals of Generative AI Practice Question
A startup is developing a customer support chatbot using Vertex AI PaLM 2 API. They notice that the model sometimes generates plausible-sounding but factually incorrect information about company policies. The chatbot currently uses no external data. To reduce these hallucinations without retraining the model, the team needs a solution that can be implemented quickly and maintains low latency. They have access to the company's internal policy database stored in Cloud SQL. Which approach should they take?
⚠ Common exam trap
Google Cloud often tests the misconception that adjusting sampling parameters (temperature, top_k) can fix factual inaccuracies, when in reality those parameters only control creativity and randomness, not knowledge grounding.
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
✓
Implement grounding by connecting the model to the company's policy database using Vertex AI Grounding.
Vertex AI Grounding connects the PaLM 2 model to the company's policy database in Cloud SQL, allowing the model to retrieve and cite factual information in real time. This approach reduces hallucinations without retraining, meets the low-latency requirement, and leverages existing internal data. Grounding works by augmenting the prompt with retrieved context from the grounding source, ensuring responses are factually grounded.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the PaLM 2 model on a dataset of company policy documents.
Why it's wrong here
Fine-tuning is costly, time-consuming, and may not fully eliminate hallucinations.
- ✓
Implement grounding by connecting the model to the company's policy database using Vertex AI Grounding.
Why this is correct
Grounding directly ties responses to verified data, reducing hallucinations effectively.
- ✗
Reduce the temperature parameter to 0 and increase top_k to 50.
Why it's wrong here
Lower temperature reduces creativity but does not add factual grounding.
- ✗
Use prompt engineering to instruct the model to only answer from its internal knowledge.
Why it's wrong here
Prompt engineering cannot reliably force the model to ignore its internal knowledge.
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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.