- A
Require lawyers to review and approve each AI-generated summary before use
In legal contexts, AI outputs must be reviewed by qualified professionals to prevent erroneous advice.
- B
Provide a confidence score for each summary but allow lawyers to bypass it
Why wrong: Confidence scores are useful but do not enforce human oversight; bypassing could lead to unchecked errors.
- C
Use a second LLM to verify the first LLM's summaries automatically
Why wrong: Automated verification lacks human judgment and accountability, which is critical for legal advice.
- D
Automatically flag summaries that contain low-confidence indicators for review
Why wrong: While helpful, this does not ensure all summaries are reviewed; high-stakes decisions need mandatory human review.
Question 140 of 997
Generative AI Leader Responsible AI and Data Governance Practice Question
This Generative AI Leader practice question tests your understanding of responsible ai and data governance. 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 startup is developing an AI tool that generates legal contract summaries. Lawyers will use these summaries for advice. What human oversight mechanism is MOST critical for responsible deployment?
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
✓
Require lawyers to review and approve each AI-generated summary before use
Option A is correct because in high-stakes domains like legal contract summarization, the AI output must be validated by a qualified human expert before use. This ensures accountability and mitigates risks of hallucinated clauses or misinterpretations that could lead to legal liability. A confidence score or automated flagging alone is insufficient because even high-confidence outputs can contain subtle errors that only a trained lawyer can catch.
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.
- ✓
Require lawyers to review and approve each AI-generated summary before use
Why this is correct
In legal contexts, AI outputs must be reviewed by qualified professionals to prevent erroneous advice.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Provide a confidence score for each summary but allow lawyers to bypass it
Why it's wrong here
Confidence scores are useful but do not enforce human oversight; bypassing could lead to unchecked errors.
- ✗
Use a second LLM to verify the first LLM's summaries automatically
Why it's wrong here
Automated verification lacks human judgment and accountability, which is critical for legal advice.
- ✗
Automatically flag summaries that contain low-confidence indicators for review
Why it's wrong here
While helpful, this does not ensure all summaries are reviewed; high-stakes decisions need mandatory human review.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that automated verification (e.g., a second LLM or confidence scoring) is sufficient for high-stakes AI outputs, when in fact only human expert review provides the necessary accountability and domain-specific validation.
Detailed technical explanation
How to think about this question
Under the hood, LLMs generate summaries via autoregressive token prediction, which can produce plausible-sounding but legally invalid clauses (e.g., misstating a liability cap). Human-in-the-loop (HITL) oversight is a core principle of the NIST AI Risk Management Framework, requiring domain experts to validate outputs in high-risk use cases. In practice, a lawyer reviewing each summary must cross-reference the original contract text, ensuring the summary preserves critical legal nuances like indemnification triggers or governing law provisions.
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.
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Responsible AI and Data Governance — study guide chapter
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Responsible AI and Data Governance practice questions
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Responsible AI and Data Governance — This question tests Responsible AI and Data Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Require lawyers to review and approve each AI-generated summary before use — Option A is correct because in high-stakes domains like legal contract summarization, the AI output must be validated by a qualified human expert before use. This ensures accountability and mitigates risks of hallucinated clauses or misinterpretations that could lead to legal liability. A confidence score or automated flagging alone is insufficient because even high-confidence outputs can contain subtle errors that only a trained lawyer can catch.
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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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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