AI0-001 AI Security, Ethics and Governance Practice Question
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?
⚠ Common exam trap
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.
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.
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
Simplifying to logistic regression sacrifices the non-linear decision boundaries required to detect the subtle, evolving adversarial evasion patterns the hacker group uses, as logistic regression models linear relationships and cannot capture the complex feature interactions a deep neural network learns. This option is tempting because logistic regression offers inherent interpretability and lower computational cost, making it the correct choice when the primary requirement is explainability and the data is linearly separable without adversarial manipulation.
- ✓
Perform adversarial training using the hacker group's known evasion patterns
Why this is correct
Adversarial training directly hardens the model against those patterns.
- ✗
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.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 754 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.