- A
Increase the number of fine‑tuning epochs to 10
Why wrong: More epochs risk overfitting and may worsen hallucinations on unseen data.
- B
Use a larger base model like Gemini Ultra without fine‑tuning
Why wrong: A larger base model may still hallucinate; without task‑specific tuning or grounding, it might not improve factual accuracy.
- C
Apply reinforcement learning from human feedback (RLHF) using a preference dataset that penalizes factual inaccuracies
RLHF can directly optimize for factual correctness by rewarding accurate summaries and penalizing hallucinations.
- D
Add more training examples from a different domain
Why wrong: Adding out‑of‑domain data can confuse the model and is not a targeted fix for hallucination.
Question 357 of 997
Generative AI Leader Generative AI Concepts and Technologies Practice Question
This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist is using Vertex AI to fine‑tune a PaLM 2 model for a legal document summarization task. They have 10,000 labeled document‑summary pairs. After supervised fine‑tuning, the model performs well on the training set but often hallucinates names and dates on unseen documents. Which next step is MOST likely to improve factual accuracy?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Apply reinforcement learning from human feedback (RLHF) using a preference dataset that penalizes factual inaccuracies
RLHF (Reinforcement Learning from Human Feedback) is specifically designed to align model outputs with human preferences, which can reduce hallucinations by penalizing factually incorrect generations. More data or longer training may not fix the underlying alignment issue. RAG is a separate approach but RLHF directly addresses the hallucination from the model's behavior.
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 number of fine‑tuning epochs to 10
Why it's wrong here
More epochs risk overfitting and may worsen hallucinations on unseen data.
- ✗
Use a larger base model like Gemini Ultra without fine‑tuning
Why it's wrong here
A larger base model may still hallucinate; without task‑specific tuning or grounding, it might not improve factual accuracy.
- ✓
Apply reinforcement learning from human feedback (RLHF) using a preference dataset that penalizes factual inaccuracies
Why this is correct
RLHF can directly optimize for factual correctness by rewarding accurate summaries and penalizing hallucinations.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add more training examples from a different domain
Why it's wrong here
Adding out‑of‑domain data can confuse the model and is not a targeted fix for hallucination.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Similar concept trap
Adding out‑of‑domain data can confuse the model and is not a targeted fix for hallucination.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Generative AI Concepts and Technologies — study guide chapter
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply reinforcement learning from human feedback (RLHF) using a preference dataset that penalizes factual inaccuracies — RLHF (Reinforcement Learning from Human Feedback) is specifically designed to align model outputs with human preferences, which can reduce hallucinations by penalizing factually incorrect generations. More data or longer training may not fix the underlying alignment issue. RAG is a separate approach but RLHF directly addresses the hallucination from the model's behavior.
What should I do if I get this Generative AI Leader question wrong?
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 →
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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