AI0-001 AI Security, Ethics and Governance Practice Question
Which TWO of the following are effective defenses against adversarial evasion attacks on image classifiers?
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
✓
Gradient masking
Adversarial training and gradient masking (e.g., defensive distillation) are common defenses. Data augmentation helps generalization but not specifically against adversarial perturbations; feature squeezing reduces input complexity; input validation is generic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data augmentation
Why it's wrong here
Data augmentation adds noise/transformations but not targeted adversarial perturbations.
- ✓
Gradient masking
Why this is correct
Gradient masking obscures gradient information to prevent crafting adversarial examples.
- ✓
Adversarial training
Why this is correct
Adversarial training includes adversarial examples in training to improve robustness.
- ✗
Input validation
Why it's wrong here
Input validation checks for malicious inputs but adversarial perturbations are often undetectable.
- ✗
Feature squeezing
Why it's wrong here
Feature squeezing reduces input precision to remove adversarial perturbations, but it is not always effective.
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Same concept, more angles
3 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 TWO of the following are best practices for securing an AI model against adversarial attacks?
easy- A.Model pruning to reduce the number of parameters.
- ✓ B.Adversarial training with perturbed examples.
- ✓ C.Input sanitization and validation.
- D.Increasing model complexity to capture more patterns.
- E.Hyperparameter optimization using grid search.
Why B: 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.
Variation 2. Which TWO of the following are effective defenses against adversarial examples in AI systems?
medium- ✓ A.Train the model with adversarial examples (adversarial training)
- B.Use an ensemble of models and majority voting
- C.Increase the model's sensitivity to input changes
- ✓ D.Implement input sanitization and feature squeezing
- E.Reduce model complexity through pruning
Why A: Adversarial training (A) is effective because it exposes the model to perturbed inputs during training, forcing it to learn robust decision boundaries that are less sensitive to small, malicious perturbations. Input sanitization and feature squeezing (D) reduce the attack surface by compressing input features (e.g., reducing bit depth or spatial smoothing) to eliminate adversarial noise before inference, making it harder for an attacker to craft a successful perturbation.
Variation 3. A healthcare AI system misdiagnosed patients due to adversarial inputs. What security measure should be prioritized?
medium- A.Encrypt all patient data
- B.Use stronger authentication
- C.Regular software updates
- ✓ D.Implement adversarial training
Why D: (Implement adversarial training) is correct because adversarial training makes the model robust to input manipulation. Option A (Encrypt all patient data) protects data privacy but not model integrity. Option B (Use stronger authentication) is for access control. Option C (Regular software updates) is general maintenance and does not specifically address adversarial inputs.
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.