- A
Switch to an unsupervised learning approach to detect anomalies
Why wrong: Unsupervised may not be effective for known threat patterns.
- B
Simplify the model to a logistic regression to reduce the attack surface
Why wrong: Simpler models may be less accurate and still vulnerable.
- C
Perform adversarial training using the hacker group's known evasion patterns
Adversarial training directly hardens the model against those patterns.
- D
Add random noise to all input data to confuse evasion attempts
Why wrong: Random noise may degrade performance and is not targeted.
Question 956 of 1,000
AI0-001 AI Security, Ethics and Governance Practice Question
This AI0-001 practice question tests your understanding of ai security, ethics and governance. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 national security agency uses AI to analyze surveillance data for threat detection. The system is deployed in a high-stakes environment where false negatives could lead to missed threats, and false positives waste analyst time. Recently, a known hacker group attempted to evade detection by subtly modifying their communication patterns over time, a form of adversarial evasion. The agency wants to harden the system while maintaining performance. The system uses a deep neural network. Which mitigation strategy is most appropriate?
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
✓
Perform adversarial training using the hacker group's known evasion patterns
Adversarial training is the most appropriate mitigation because it directly incorporates known evasion patterns into the training process, making the deep neural network robust to the hacker group's subtle modifications. By retraining the model on adversarial examples, the decision boundary is hardened against these specific attacks without sacrificing overall detection performance. This approach is a standard defense in high-stakes security AI, balancing false positive and false negative rates while countering adversarial evasion.
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.
- ✗
Switch to an unsupervised learning approach to detect anomalies
Why it's wrong here
Unsupervised may not be effective for known threat patterns.
- ✗
Simplify the model to a logistic regression to reduce the attack surface
Why it's wrong here
Simpler models may be less accurate and still vulnerable.
- ✓
Perform adversarial training using the hacker group's known evasion patterns
Why this is correct
Adversarial training directly hardens the model against those patterns.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add random noise to all input data to confuse evasion attempts
Why it's wrong here
Random noise may degrade performance and is not targeted.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that simplifying a model (e.g., to logistic regression) reduces attack surface, but in adversarial evasion, simpler models are actually more vulnerable because they lack the capacity to learn robust decision boundaries against crafted perturbations.
Detailed technical explanation
How to think about this question
Adversarial training works by augmenting the training dataset with perturbed samples (e.g., using Fast Gradient Sign Method or Projected Gradient Descent) that maximize loss, forcing the model to learn robust features. In practice, this technique can reduce attack success rates from over 90% to below 10% on deep neural networks, but requires careful tuning to avoid overfitting to specific perturbations. Real-world deployments, such as in malware detection, often combine adversarial training with input sanitization and ensemble methods for layered defense.
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.
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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: Perform adversarial training using the hacker group's known evasion patterns — Adversarial training is the most appropriate mitigation because it directly incorporates known evasion patterns into the training process, making the deep neural network robust to the hacker group's subtle modifications. By retraining the model on adversarial examples, the decision boundary is hardened against these specific attacks without sacrificing overall detection performance. This approach is a standard defense in high-stakes security AI, balancing false positive and false negative rates while countering adversarial evasion.
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.
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Last reviewed: Jul 4, 2026
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