- A
A human-in-the-loop review for all loan decisions
Human oversight ensures accountability and compliance with the EU AI Act.
- B
Publish the model's accuracy metrics on a public website
Why wrong: Transparency is important but does not fulfill the human oversight requirement.
- C
A fully automated decision process with no human involvement
Why wrong: High-risk systems require human oversight.
- D
Regular bias audits without human review of individual decisions
Why wrong: Bias audits are necessary but do not replace the requirement for human oversight of decisions.
Question 734 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.
An organization uses a generative AI model to automatically approve or reject loan applications. To comply with the EU AI Act's requirements for high-risk AI systems, what must 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
✓
A human-in-the-loop review for all loan decisions
The EU AI Act mandates that high-risk AI systems, such as those used for credit scoring and loan approvals, must include human oversight to mitigate risks of automated bias and errors. A human-in-the-loop (HITL) review ensures that each loan decision is subject to human judgment, allowing for intervention in edge cases or when the model's confidence is low. This directly satisfies the Act's requirement for meaningful human control over high-risk AI outputs.
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.
- ✓
A human-in-the-loop review for all loan decisions
Why this is correct
Human oversight ensures accountability and compliance with the EU AI Act.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Publish the model's accuracy metrics on a public website
Why it's wrong here
Transparency is important but does not fulfill the human oversight requirement.
- ✗
A fully automated decision process with no human involvement
Why it's wrong here
High-risk systems require human oversight.
- ✗
Regular bias audits without human review of individual decisions
Why it's wrong here
Bias audits are necessary but do not replace the requirement for human oversight of decisions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that transparency measures (like publishing metrics) or bias audits alone are sufficient for compliance, when the EU AI Act specifically requires human oversight for high-risk systems, making human-in-the-loop review the mandatory control.
Detailed technical explanation
How to think about this question
Under the EU AI Act, high-risk systems must implement a human oversight mechanism that can override or reverse the system's output, often achieved through a HITL architecture where a human reviewer validates the model's decision before final execution. In practice, this involves setting confidence thresholds: if the model's probability for a decision falls below a certain value (e.g., 0.85), the case is automatically routed to a human for manual review. This prevents the model from making high-stakes errors in ambiguous scenarios, such as borderline creditworthiness cases where the model might exhibit hidden bias.
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: A human-in-the-loop review for all loan decisions — The EU AI Act mandates that high-risk AI systems, such as those used for credit scoring and loan approvals, must include human oversight to mitigate risks of automated bias and errors. A human-in-the-loop (HITL) review ensures that each loan decision is subject to human judgment, allowing for intervention in edge cases or when the model's confidence is low. This directly satisfies the Act's requirement for meaningful human control over high-risk AI outputs.
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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