- A
Enable Vertex AI Grounding with a curated database of documents
Grounding retrieves evidence to reduce hallucinations.
- B
Increase the temperature parameter to make the model more confident
Why wrong: Higher temperature increases creativity and can worsen hallucinations.
- C
Add more safety filters to block uncertain responses
Why wrong: Safety filters block harmful content, not factual errors.
- D
Fine-tune the model on a high-quality dataset of correct summaries
Why wrong: Fine-tuning requires significant time and resources.
Question 797 of 997
Reducing Hallucinations with Vertex AI Grounding
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 company is using Vertex AI Model Garden to deploy a foundation model for document summarization. They notice that the model sometimes generates summaries that include factual errors. They want to reduce hallucinations without sacrificing latency. Which approach should they try first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Quick Answer
The correct answer is to enable Vertex AI Grounding with a curated database of documents, as this approach directly reduces hallucinations by anchoring the model’s output to verified, real-time facts without the latency penalty of retraining. Grounding works by retrieving relevant snippets from a trusted knowledge base during inference, allowing the model to cross-check its generated summaries against authoritative sources, which is far more efficient than fine-tuning or adjusting parameters. On the Google Cloud Generative AI Leader exam, this question tests your understanding of how to balance accuracy and performance in production deployments—a common trap is assuming fine-tuning is the default fix, but it is resource-intensive and may not eliminate factual errors, while safety filters address harm, not truth. Remember the mnemonic “Ground First, Tune Last” to recall that grounding with a curated source is the quickest, lowest-latency path to factual reliability.
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 curated database of documents
Vertex AI Grounding connects the model to a curated database of documents, allowing it to retrieve and cite factual information in real-time. This directly reduces hallucinations by grounding responses in verified sources without adding significant latency, as the retrieval step is optimized for speed. Other approaches either increase latency (fine-tuning), reduce output quality (temperature increase), or do not address factual accuracy (safety filters).
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.
- ✓
Enable Vertex AI Grounding with a curated database of documents
Why this is correct
Grounding retrieves evidence to reduce hallucinations.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the temperature parameter to make the model more confident
Why it's wrong here
Higher temperature increases creativity and can worsen hallucinations.
- ✗
Add more safety filters to block uncertain responses
Why it's wrong here
Safety filters block harmful content, not factual errors.
- ✗
Fine-tune the model on a high-quality dataset of correct summaries
Why it's wrong here
Fine-tuning requires significant time and resources.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume fine-tuning is the default fix for hallucinations, but the question prioritizes latency and immediate factual grounding, making RAG via Vertex AI Grounding the faster and more appropriate first step.
Detailed technical explanation
How to think about this question
Vertex AI Grounding uses a retrieval-augmented generation (RAG) architecture where the model queries a vector database (e.g., using embeddings and approximate nearest neighbor search) to fetch relevant documents before generating a response. This ensures the model's output is anchored to specific source text, and the grounding process typically adds only a few hundred milliseconds to latency, making it suitable for real-time summarization. In contrast, fine-tuning adjusts model weights offline and cannot adapt to new or updated documents without retraining.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
- →
Google Cloud's Generative AI Offerings — study guide chapter
Learn the concepts, then practise the questions
- →
Google Cloud's Generative AI Offerings practice questions
Targeted practice on this topic area only
- →
All Generative AI Leader questions
997 questions across all exam domains
- →
Google Cloud Generative AI Leader Generative AI Leader study guide
Full concept coverage aligned to exam objectives
- →
Generative AI Leader practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related Generative AI Leader practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Fundamentals of Generative AI practice questions
Practise Generative AI Leader questions linked to Fundamentals of Generative AI.
Business Strategies for Generative AI Solutions practice questions
Practise Generative AI Leader questions linked to Business Strategies for Generative AI Solutions.
Generative AI Concepts and Technologies practice questions
Practise Generative AI Leader questions linked to Generative AI Concepts and Technologies.
Google AI Ecosystem and Strategy practice questions
Practise Generative AI Leader questions linked to Google AI Ecosystem and Strategy.
Responsible AI and Data Governance practice questions
Practise Generative AI Leader questions linked to Responsible AI and Data Governance.
Google Cloud's Generative AI Offerings practice questions
Practise Generative AI Leader questions linked to Google Cloud's Generative AI Offerings.
Techniques to Improve Generative AI Model Output practice questions
Practise Generative AI Leader questions linked to Techniques to Improve Generative AI Model Output.
Applying Generative AI in Business practice questions
Practise Generative AI Leader questions linked to Applying Generative AI in Business.
Generative AI Leader fundamentals practice questions
Practise Generative AI Leader questions linked to Generative AI Leader fundamentals.
Generative AI Leader scenario practice questions
Practise Generative AI Leader questions linked to Generative AI Leader scenario.
Generative AI Leader troubleshooting practice questions
Practise Generative AI Leader questions linked to Generative AI Leader troubleshooting.
Practice this exam
Start a free Generative AI Leader practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable Vertex AI Grounding with a curated database of documents — Vertex AI Grounding connects the model to a curated database of documents, allowing it to retrieve and cite factual information in real-time. This directly reduces hallucinations by grounding responses in verified sources without adding significant latency, as the retrieval step is optimized for speed. Other approaches either increase latency (fine-tuning), reduce output quality (temperature increase), or do not address factual accuracy (safety filters).
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More Generative AI Leader practice questions
- A data scientist is trying to get online predictions from a Vertex AI endpoint but receives the error shown. What is the…
- A data scientist notices that a text generation model deployed on Vertex AI returns repetitive outputs after a few turns…
- A company is deploying a generative AI model for medical diagnosis support. Which THREE considerations are critical for…
- Which THREE considerations are critical when deploying a generative AI model using Vertex AI Endpoints for a latency-sen…
- A company is deploying a generative AI model for customer support. They want to reduce hallucinations while maintaining…
- Which TWO techniques are commonly used to control the style and tone of a generative model's output?
Last reviewed: Jul 4, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.