- A
The system must be explainable using chain-of-thought reasoning
Why wrong: Explainability is required but not specifically chain-of-thought; any appropriate method works.
- B
The system must achieve a minimum accuracy of 90% on validation data
Why wrong: The Act does not prescribe specific numerical thresholds; it requires appropriate accuracy and robustness.
- C
The system must be trained on data that is representative of the target population
Why wrong: Data governance is important but not explicitly enumerated as a separate requirement; it falls under data governance aspects.
- D
The system must undergo a conformity assessment before deployment
High-risk AI systems must undergo conformity assessment to ensure compliance with the Act.
Question 698 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 cloud architect is designing a generative AI pipeline that must comply with the EU AI Act for high-risk AI systems. Which of the following is a mandatory requirement under the Act?
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
✓
The system must undergo a conformity assessment before deployment
Under the EU AI Act, high-risk AI systems must undergo a conformity assessment before deployment to ensure compliance with requirements such as risk management, data governance, and transparency. This is a mandatory procedural step, not a performance metric or specific reasoning technique. The assessment may involve self-evaluation or third-party review depending on the system's risk category.
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.
- ✗
The system must be explainable using chain-of-thought reasoning
Why it's wrong here
Explainability is required but not specifically chain-of-thought; any appropriate method works.
- ✗
The system must achieve a minimum accuracy of 90% on validation data
Why it's wrong here
The Act does not prescribe specific numerical thresholds; it requires appropriate accuracy and robustness.
- ✗
The system must be trained on data that is representative of the target population
Why it's wrong here
Data governance is important but not explicitly enumerated as a separate requirement; it falls under data governance aspects.
- ✓
The system must undergo a conformity assessment before deployment
Why this is correct
High-risk AI systems must undergo conformity assessment to ensure compliance with the Act.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between aspirational best practices (like explainability or accuracy thresholds) and actual legal mandates, leading candidates to pick a plausible-sounding but non-mandatory option like A or B instead of the procedural requirement in D.
Detailed technical explanation
How to think about this question
The conformity assessment under the EU AI Act is documented in Articles 43 and 19, requiring high-risk systems to demonstrate compliance with requirements such as risk management (Article 9), data governance (Article 10), and transparency (Article 13). In practice, this involves creating a technical documentation package, including a description of the system's design, training data, and performance metrics, which is then reviewed either internally or by a notified body. For example, a generative AI model used in medical diagnostics would need to prove that its training data is representative of diverse patient demographics and that its outputs are traceable and auditable.
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: The system must undergo a conformity assessment before deployment — Under the EU AI Act, high-risk AI systems must undergo a conformity assessment before deployment to ensure compliance with requirements such as risk management, data governance, and transparency. This is a mandatory procedural step, not a performance metric or specific reasoning technique. The assessment may involve self-evaluation or third-party review depending on the system's risk category.
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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