The answer is to implement grounding by connecting the model to the company’s policy database using Vertex AI Grounding. This is correct because Vertex AI Grounding augments the prompt with real-time, retrieved context from the specified data source—here, the Cloud SQL policy database—without requiring any model retraining. By fetching and citing factual information during inference, it directly addresses hallucinations while maintaining low latency, as the retrieval step is optimized for speed. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of how grounding differs from fine-tuning or prompt engineering; a common trap is assuming you must retrain or use a vector database when a structured SQL source is available. Remember the key distinction: grounding retrieves facts at inference time, while fine-tuning memorizes them during training. For a quick memory tip, think “Grounding grabs, retraining grinds.”
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Vertex AI Grounding uses a retrieval-augmented generation (RAG) architecture: the user query is sent to a retrieval service that searches the Cloud SQL database (via a vector index or structured query), fetches relevant policy snippets, and injects them into the prompt context for the PaLM 2 model. This ensures the model's output is grounded in the retrieved documents, and citations can be provided. The latency is kept low because retrieval is optimized with approximate nearest neighbor (ANN) search and caching, and the model only generates from the augmented context.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What does this Generative AI Leader question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement grounding by connecting the model to the company's policy database using Vertex AI Grounding. — Option B is correct because 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.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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