AI0-001 AI Security, Ethics and Governance Practice Question
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 TWO of the following are best practices for securing an AI model against adversarial attacks?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
Option B is correct because 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.
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.
✗
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Input sanitization and validation.
Why this is correct
Sanitizing inputs can remove adversarial perturbations before they reach the model.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increasing model complexity to capture more patterns.
Why it's wrong here
Increased complexity can make the model more sensitive to small perturbations, making attacks easier.
✗
Hyperparameter optimization using grid search.
Why it's wrong here
Hyperparameter optimization does not directly defend against adversarial attacks.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Adversarial training works by solving a min-max optimization problem where the model is trained on worst-case perturbations within an epsilon-ball (e.g., L-infinity norm bound of 0.3 for images). In practice, this can be implemented using the Fast Gradient Sign Method (FGSM) or Projected Gradient Descent (PGD) during training, which forces the model to learn smoother decision boundaries. A real-world scenario is a facial recognition system that must resist adversarial stickers or patches; adversarial training with those specific perturbations can reduce attack success rates from >90% to <10%.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Adversarial training with perturbed examples. — Option B is correct because 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.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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