- A
Transparency
Transparency is about openness in AI systems' workings and decisions.
- B
Scalability
Why wrong: Scalability is a technical requirement, not an ethical principle.
- C
Accountability
Accountability means taking responsibility for AI system outcomes.
- D
Latency
Why wrong: Latency is a performance measure, not ethics.
- E
Fairness
Fairness ensures unbiased and equitable outcomes.
Question 764 of 1,000
Core Principles of AI Ethics
This AI0-001 practice question tests your understanding of ai security, ethics and governance. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 principles of AI ethics as defined by major frameworks?
Quick Answer
The answer is fairness, transparency, and accountability. These three are the core principles of AI ethics because they directly address the moral and governance concerns of AI systems: fairness prevents algorithmic bias, transparency ensures decisions can be understood and audited, and accountability establishes clear responsibility for outcomes. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish ethical guardrails from technical performance metrics—a common trap is confusing scalability or latency, which are about system speed and capacity, with ethics. To remember the trio, think of the acronym FTA: Fairness, Transparency, Accountability—these are the pillars that keep AI trustworthy, not just fast.
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
✓
Transparency
Transparency is a core principle of AI ethics because it requires that AI systems be open about their purpose, data sources, and decision-making processes. Major frameworks like the OECD AI Principles and the EU Ethics Guidelines for Trustworthy AI emphasize transparency to enable auditability and informed consent. Without transparency, stakeholders cannot verify that an AI system operates as intended or identify potential biases.
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.
- ✓
Transparency
Why this is correct
Transparency is about openness in AI systems' workings and decisions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Scalability
Why it's wrong here
Scalability is a technical requirement, not an ethical principle.
- ✓
Accountability
Why this is correct
Accountability means taking responsibility for AI system outcomes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Latency
Why it's wrong here
Latency is a performance measure, not ethics.
- ✓
Fairness
Why this is correct
Fairness ensures unbiased and equitable outcomes.
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 candidates by mixing technical performance metrics (scalability, latency) with ethical principles, expecting you to recognize that only value-based concepts like transparency, accountability, and fairness belong to AI ethics frameworks.
Detailed technical explanation
How to think about this question
Under the hood, transparency in AI often involves explainability techniques such as LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations) to generate post-hoc explanations for model predictions. In a real-world scenario, a credit scoring AI must disclose the key factors (e.g., income, payment history) that influenced a denial to comply with regulations like the EU's GDPR 'right to explanation'. This goes beyond simple logging to require interpretable model outputs that non-experts can understand.
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 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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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AI Security, Ethics and Governance — study guide chapter
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Transparency — Transparency is a core principle of AI ethics because it requires that AI systems be open about their purpose, data sources, and decision-making processes. Major frameworks like the OECD AI Principles and the EU Ethics Guidelines for Trustworthy AI emphasize transparency to enable auditability and informed consent. Without transparency, stakeholders cannot verify that an AI system operates as intended or identify potential biases.
What should I do if I get this AI0-001 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
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
1 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 THREE of the following are key principles of trustworthy AI as defined by major regulatory bodies?
medium- ✓ A.Fairness and non-discrimination
- ✓ B.Transparency and explainability
- C.Maximum profitability
- D.Proprietary secrecy
- ✓ E.Accountability
Why A: Fairness and non-discrimination (A) is a core principle of trustworthy AI because regulatory bodies like the European Commission's High-Level Expert Group on AI and the OECD require that AI systems do not perpetuate or amplify biases against protected groups. This involves implementing bias detection and mitigation techniques during model training and validation, such as using fairness metrics like demographic parity or equalized odds to ensure equitable outcomes across different demographic segments.
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Last reviewed: Jul 4, 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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