- A
Cloud infrastructure configuration
Why wrong: Infrastructure is operational, not governance.
- B
Model accuracy benchmarks
Why wrong: Accuracy is evaluated during development, not a governance component.
- C
Risk assessment and mitigation plans
Essential for identifying and managing AI risks.
- D
Transparency and explainability policies
Required for accountability and trust.
- E
Data management and privacy controls
Ensures data quality and regulatory compliance.
Quick Answer
The correct answer is risk assessment, data management and privacy controls, and transparency. These three are key components of an AI governance framework because governance focuses on the policies and processes that ensure ethical, legal, and responsible AI use, not on technical performance or deployment specifics. Risk assessment identifies potential harms and biases, data management governs how data is collected, stored, and used with privacy controls, and transparency ensures decisions are explainable and auditable. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish governance pillars from operational metrics or infrastructure—a common trap is confusing model accuracy, which measures performance, with governance, which oversees accountability. Similarly, cloud infrastructure is a deployment choice, not a governance component. To remember, think of the three G’s: Governance requires Guardrails (risk), Guidance (data/privacy), and Glass (transparency).
AI0-001 AI Security, Ethics and Governance Practice Question
This AI0-001 practice question tests your understanding of ai security, ethics and 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.
Which THREE of the following are key components of an AI governance framework?
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
Risk assessment and mitigation plans
Options A, B, and D are correct because risk assessment, data management, and transparency are core pillars. Option C is wrong because model accuracy is a performance metric, not a governance component. Option E is wrong because cloud infrastructure is deployment, not governance.
Key principle: Authentication proves identity; authorization controls what that identity can do after login. Both must work for full privileged access.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud infrastructure configuration
Why it's wrong here
Infrastructure is operational, not governance.
- ✗
Model accuracy benchmarks
Why it's wrong here
Accuracy is evaluated during development, not a governance component.
- ✓
Risk assessment and mitigation plans
Why this is correct
Essential for identifying and managing AI risks.
Related concept
Authentication checks who the user is.
- ✓
Transparency and explainability policies
Why this is correct
Required for accountability and trust.
Related concept
Authentication checks who the user is.
- ✓
Data management and privacy controls
Why this is correct
Ensures data quality and regulatory compliance.
Related concept
Authentication checks who the user is.
Common exam traps
Common exam trap: authentication is not authorization
Logging in proves the user can authenticate. It does not automatically mean the user is allowed to enter privileged or configuration mode. Watch for AAA authorization, privilege level and command authorization details.
Detailed technical explanation
How to think about this question
This kind of question is testing the difference between identity and permission. A user may successfully log in to a router because authentication is working, but still fail to enter configuration mode because authorization is missing, misconfigured or mapped to a lower privilege level.
KKey Concepts to Remember
- Authentication checks who the user is.
- Authorization controls what the user is allowed to do after login.
- Privilege levels affect access to EXEC and configuration commands.
- AAA, TACACS+ and RADIUS can separate login success from command access.
TExam Day Tips
- Do not assume successful login means full administrative access.
- Look for words such as cannot enter configuration mode, privilege level, authorization or command access.
- Separate login problems from permission problems before choosing the answer.
Key takeaway
Authentication proves identity; authorization controls what that identity can do after login. Both must work for full privileged access.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Authentication proves identity; authorization controls what that identity can do after login. Both must work for full privileged access. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Review Cisco AAA concepts — authentication, authorization, and accounting. Study privilege levels (0–15), command authorization under TACACS+, and how RADIUS differs. Then practise related AI0-001 questions on access control and AAA configuration.
- →
AI Security, Ethics and Governance — study guide chapter
Learn the concepts, then practise the questions
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AI Security, Ethics and Governance practice questions
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Authentication checks who the user is..
What is the correct answer to this question?
The correct answer is: Risk assessment and mitigation plans — Options A, B, and D are correct because risk assessment, data management, and transparency are core pillars. Option C is wrong because model accuracy is a performance metric, not a governance component. Option E is wrong because cloud infrastructure is deployment, not governance.
What should I do if I get this AI0-001 question wrong?
Review Cisco AAA concepts — authentication, authorization, and accounting. Study privilege levels (0–15), command authorization under TACACS+, and how RADIUS differs. Then practise related AI0-001 questions on access control and AAA configuration.
What is the key concept behind this question?
Authentication checks who the user is.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO of the following are essential components of a responsible AI governance framework?
easy- ✓ A.Assignment of a responsible owner for each AI system's outcomes
- B.Using ensemble methods to reduce overfitting
- ✓ C.Clear documentation of model development and decision-making processes
- D.Automated hyperparameter tuning to improve accuracy
- E.Deploying models on dedicated hardware to reduce latency
Why A: Options A and D are correct because transparency in model decisions and accountability for AI outcomes are foundational to responsible AI governance. Options B, C, and E are important but are more operational or technical rather than core governance components.
Variation 2. Which THREE of the following are key components of an AI governance framework?
medium- ✓ A.Regular auditing and monitoring for compliance.
- B.Cloud-based deployment for scalability.
- ✓ C.Ethical guidelines for AI development and deployment.
- ✓ D.Explainability mechanisms for model decisions.
- E.Model accuracy thresholds for production deployment.
Why A: Regular auditing and monitoring for compliance (A) is a key component of an AI governance framework because it ensures that AI systems operate within legal, ethical, and organizational policies over time. Continuous monitoring detects drift, bias, or security violations, while audits provide evidence of adherence to standards such as ISO/IEC 42001 or internal governance rules. Without this, governance becomes a static policy with no enforcement or verification.
Last reviewed: Jun 23, 2026
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.
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