AI0-001 AI Security, Ethics and Governance Practice Question
Which TWO of the following are best practices for securing an AI model against adversarial attacks?
⚠ Common exam trap
CompTIA often tests the misconception that increasing model complexity or pruning improves security, when in fact these techniques address performance or efficiency, not adversarial robustness.
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
✓
Adversarial training with perturbed examples.
Adversarial training explicitly augments the training dataset with perturbed examples (e.g., using FGSM or PGD attacks) to teach the model to recognize and resist malicious inputs. This method directly hardens the model against evasion attacks by improving its decision boundary robustness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model pruning to reduce the number of parameters.
Why it's wrong here
Pruning can remove neurons that are critical for detecting adversarial perturbations, reducing robustness.
- ✓
Adversarial training with perturbed examples.
Why this is correct
Adversarial training exposes the model to adversarial inputs, improving robustness.
- ✓
Input sanitization and validation.
Why this is correct
Sanitizing inputs can remove adversarial perturbations before they reach the model.
- ✗
Increasing model complexity to capture more patterns.
Why it's wrong here
Increasing model complexity enlarges the attack surface by introducing more parameters for gradient-based adversarial perturbations to exploit, directly contradicting the security requirement for robustness. This option is tempting because deeper models often improve accuracy on benign data, making it a correct choice for performance optimisation tasks where generalisation, not adversarial defence, is the primary goal.
- ✗
Hyperparameter optimization using grid search.
Why it's wrong here
Hyperparameter optimization does not directly defend against adversarial attacks.
About these practice questions
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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.