- A
Use a model with high confidence scores to bypass human review
Why wrong: Even high confidence does not eliminate the need for human oversight in high-risk domains.
- B
Allow the AI to act autonomously for low-risk cases to reduce workload
Why wrong: Medical diagnosis is generally high-risk; low-risk cases may still need oversight.
- C
Require a human clinician to review all AI-generated diagnoses before acting on them
High-stakes decisions need human review.
- D
Provide an override mechanism that allows the human to reject the AI's recommendation
Override mechanisms are required for human oversight.
- E
Document the human-in-the-loop process and roles clearly
Documentation is important for accountability and regulatory compliance.
Question 579 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 company is deploying a generative AI model for medical diagnosis assistance. To comply with both Google's AI Principles and emerging regulations (e.g., EU AI Act), they must ensure appropriate human oversight. Which THREE measures should they implement?
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 a human clinician to review all AI-generated diagnoses before acting on them
Option C is correct because requiring a human clinician to review all AI-generated diagnoses before acting on them directly implements the human oversight mandate of Google's AI Principles (specifically the 'be accountable to people' principle) and the EU AI Act's requirement for high-risk AI systems to have meaningful human review. This ensures that the model's output is validated by a domain expert, mitigating risks of false positives or negatives that could harm patients.
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.
- ✗
Use a model with high confidence scores to bypass human review
Why it's wrong here
Even high confidence does not eliminate the need for human oversight in high-risk domains.
- ✗
Allow the AI to act autonomously for low-risk cases to reduce workload
Why it's wrong here
Medical diagnosis is generally high-risk; low-risk cases may still need oversight.
- ✓
Require a human clinician to review all AI-generated diagnoses before acting on them
Why this is correct
High-stakes decisions need human review.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Provide an override mechanism that allows the human to reject the AI's recommendation
Why this is correct
Override mechanisms are required for human oversight.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Document the human-in-the-loop process and roles clearly
Why this is correct
Documentation is important for accountability and regulatory compliance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates mistakenly believe high confidence scores or low-risk classifications can justify removing human oversight, but the exam tests that Google's AI Principles and regulatory frameworks like the EU AI Act require human review for all high-risk AI outputs regardless of confidence or perceived risk level.
Detailed technical explanation
How to think about this question
Under the hood, human-in-the-loop (HITL) systems for medical AI typically implement a 'reject option' where the model flags low-confidence predictions for mandatory human review, while high-confidence predictions may still require human sign-off due to regulatory requirements. In practice, a real-world scenario involves a radiology AI that highlights suspicious regions but requires a radiologist to confirm each finding before it is included in the patient report, ensuring accountability and traceability as mandated by the EU AI Act's transparency obligations.
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.
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Responsible AI and Data Governance — study guide chapter
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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 a human clinician to review all AI-generated diagnoses before acting on them — Option C is correct because requiring a human clinician to review all AI-generated diagnoses before acting on them directly implements the human oversight mandate of Google's AI Principles (specifically the 'be accountable to people' principle) and the EU AI Act's requirement for high-risk AI systems to have meaningful human review. This ensures that the model's output is validated by a domain expert, mitigating risks of false positives or negatives that could harm patients.
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
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