- A
Profitability
Why wrong: Profitability is a business objective, not an ethical principle.
- B
Robustness
Robustness ensures AI systems perform reliably under varied conditions.
- C
Transparency
Transparency is a core OECD principle.
- D
Scalability
Why wrong: Scalability is a technical requirement, not an ethical principle.
- E
Accountability
Accountability assigns responsibility for AI system outcomes.
Question 188 of 1,000
OECD Trustworthy AI Principles — Guide | CompTIA AI+ Explained
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 are key principles of trustworthy AI according to the OECD?
Quick Answer
The answer is transparency, accountability, and robustness. These are three of the five core OECD trustworthy AI principles, which also include inclusive growth and human-centred values. The OECD framework defines trustworthy AI as systems that are lawful, technically robust, and socially responsible, with transparency requiring clear disclosure of AI capabilities and limitations, accountability demanding clear allocation of responsibility for outcomes, and robustness ensuring systems perform reliably under adverse conditions. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish the official OECD principles from common distractors like profitability or scalability, which are business goals rather than ethical mandates. A frequent trap is confusing accountability with profitability, so remember that the OECD principles focus on societal trust, not financial returns. To lock in the three, use the mnemonic “TAR” for Transparency, Accountability, and Robustness—the ethical tripod of trustworthy AI.
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
✓
Robustness
Robustness is a key principle of trustworthy AI according to the OECD, ensuring that AI systems operate reliably and securely under a wide range of conditions, including handling errors, adversarial inputs, and unexpected scenarios. This principle directly supports the goal of maintaining system integrity and preventing harm, which is fundamental to trustworthiness.
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.
- ✗
Profitability
Why it's wrong here
Profitability is a business objective, not an ethical principle.
- ✓
Robustness
Why this is correct
Robustness ensures AI systems perform reliably under varied conditions.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Transparency
Why this is correct
Transparency is a core OECD principle.
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 assigns responsibility for AI system 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 including plausible-sounding business or operational terms like 'profitability' or 'scalability' as distractors, leading them to confuse general system attributes with the specific ethical and governance principles outlined by the OECD.
Detailed technical explanation
How to think about this question
Robustness in AI involves techniques such as adversarial training, input validation, and redundancy to ensure the model performs consistently despite noise or malicious perturbations. For example, in image classification, a robust model should resist small pixel-level changes that could otherwise cause misclassification, a subtle behavior that is critical in safety-critical applications like autonomous driving or medical diagnosis.
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
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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: Robustness — Robustness is a key principle of trustworthy AI according to the OECD, ensuring that AI systems operate reliably and securely under a wide range of conditions, including handling errors, adversarial inputs, and unexpected scenarios. This principle directly supports the goal of maintaining system integrity and preventing harm, which is fundamental to trustworthiness.
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
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Last reviewed: Jul 4, 2026
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