AI0-001 AI Governance and Ethics Practice Question
A company is developing an AI policy. Which of the following should be included to ensure accountability for AI-driven decisions?
⚠ Common exam trap
Candidates often confuse the components of an AI governance policy, such as acceptable use or data sources, with the accountability mechanism of human oversight. The key is to remember that accountability requires designated roles with authority to review and override AI decisions.
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
✓
Designated roles for human oversight and decision authority
Accountability for AI-driven decisions requires clear assignment of human roles with oversight and decision authority. This ensures that there is a responsible party who can review, override, or be held liable for the AI's outputs, which is a core principle of AI governance frameworks such as the NIST AI Risk Management Framework.
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 set of acceptable use cases for the AI
Why it's wrong here
Acceptable use cases define permitted applications, not who answers for outcomes; they leave decision ownership unassigned. They are tempting because use-case catalogues genuinely govern employee behaviour and would be the right inclusion when scoping permitted tools or prohibiting specific practices. Accountability instead requires named roles, approval authorities and escalation paths for AI-driven decisions.
- ✗
A description of the model architecture used
Why it's wrong here
Documenting model architecture addresses technical design, not accountability for decisions. It is tempting because architecture descriptions belong in model cards and technical governance artefacts, and would be the right inclusion when the policy must support reproducibility, audit of training approaches, or engineering review. Accountability instead requires named owners and escalation paths for AI-driven outcomes.
- ✗
A list of approved training data sources
Why it's wrong here
A list of approved training data sources governs data provenance and quality, not decision accountability. It is tempting because such inventories are genuinely required for bias mitigation and regulatory data-governance audits, where traceability of training inputs matters. Here, however, the stem demands named responsibility for AI-driven outcomes, which only human oversight roles and escalation paths provide.
- ✓
Designated roles for human oversight and decision authority
Why this is correct
Assigning designated roles for human oversight and decision authority establishes named accountability for AI-driven outcomes, ensuring a responsible person can review, approve or override decisions. This satisfies the policy requirement for accountability, unlike generic principles or training mandates.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.