- A
Increase the temperature to 1.0 and lower top_p to 0.1.
Why wrong: This will likely increase hallucinations.
- B
Enable grounding with Google Search to provide factual source context.
Grounding connects the model to verified information, reducing hallucination.
- C
Fine-tune the model on a large dataset of articles and human-written summaries.
Why wrong: Fine-tuning is costly and may not fix hallucination if the dataset is not carefully curated.
- D
Lower the temperature to 0.0 and increase top_p to 1.0.
Why wrong: Lower temperature reduces randomness but can still hallucinate; top_p increase doesn't help.
Question 81 of 997
Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
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 news organization is using Vertex AI Gemini to summarize articles. They observe that the summaries sometimes contain hallucinated facts—specifically, dates and statistics that are not in the original article. The team is using the default temperature and top_p settings. They want to reduce hallucinations without making summaries too repetitive or overly conservative. They also need to keep latency low. Which action should they take?
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 grounding with Google Search to provide factual source context.
Enabling grounding with Google Search is the correct action because it directly addresses the root cause of hallucinations—lack of factual source context—by allowing the model to cross-reference generated content with real-time, authoritative web data. This approach reduces fabricated dates and statistics without requiring changes to temperature or top_p, which could introduce repetition or conservatism, and it maintains low latency by leveraging Google's infrastructure for retrieval rather than model retraining.
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.
- ✗
Increase the temperature to 1.0 and lower top_p to 0.1.
Why it's wrong here
This will likely increase hallucinations.
- ✓
Enable grounding with Google Search to provide factual source context.
Why this is correct
Grounding connects the model to verified information, reducing hallucination.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Fine-tune the model on a large dataset of articles and human-written summaries.
Why it's wrong here
Fine-tuning is costly and may not fix hallucination if the dataset is not carefully curated.
- ✗
Lower the temperature to 0.0 and increase top_p to 1.0.
Why it's wrong here
Lower temperature reduces randomness but can still hallucinate; top_p increase doesn't help.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume reducing randomness (lower temperature) or increasing determinism (top_p adjustments) will fix hallucinations, but these parameters control output style, not factual grounding, which requires external verification.
Detailed technical explanation
How to think about this question
Grounding with Google Search works by attaching retrieved snippets from web search results to the prompt as context, which the model uses as a factual anchor during generation—this is similar to retrieval-augmented generation (RAG) but integrated natively in Vertex AI. Under the hood, the model's attention mechanism prioritizes the grounded context over its parametric knowledge, reducing the likelihood of inventing dates or statistics. In a real-world scenario, a news organization summarizing a breaking story would see immediate improvement because the model can verify facts against live sources, whereas temperature adjustments only affect output randomness, not factual accuracy.
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
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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 grounding with Google Search to provide factual source context. — Enabling grounding with Google Search is the correct action because it directly addresses the root cause of hallucinations—lack of factual source context—by allowing the model to cross-reference generated content with real-time, authoritative web data. This approach reduces fabricated dates and statistics without requiring changes to temperature or top_p, which could introduce repetition or conservatism, and it maintains low latency by leveraging Google's infrastructure for retrieval rather than model retraining.
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.
About these practice questions
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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.
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