- A
Train the model with adversarial examples (adversarial training)
Adversarial training hardens the model against perturbations.
- B
Use an ensemble of models and majority voting
Why wrong: Ensembles do not inherently protect against adversarial attacks.
- C
Increase the model's sensitivity to input changes
Why wrong: Higher sensitivity increases vulnerability to perturbations.
- D
Implement input sanitization and feature squeezing
Sanitization reduces the space for adversarial perturbations.
- E
Reduce model complexity through pruning
Why wrong: Pruning may inadvertently remove robustness.
Question 633 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. 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 effective defenses against adversarial examples in AI systems?
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
✓
Train the model with adversarial examples (adversarial training)
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.
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.
- ✓
Train the model with adversarial examples (adversarial training)
Why this is correct
Adversarial training hardens the model against perturbations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use an ensemble of models and majority voting
Why it's wrong here
Ensembles do not inherently protect against adversarial attacks.
- ✗
Increase the model's sensitivity to input changes
Why it's wrong here
Higher sensitivity increases vulnerability to perturbations.
- ✓
Implement input sanitization and feature squeezing
Why this is correct
Sanitization reduces the space for adversarial perturbations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce model complexity through pruning
Why it's wrong here
Pruning may inadvertently remove robustness.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that ensemble methods or model simplification inherently improve adversarial robustness, when in fact they do not address the fundamental mechanism of adversarial perturbations and may even weaken defenses.
Detailed technical explanation
How to think about this question
Adversarial training often uses the Fast Gradient Sign Method (FGSM) or Projected Gradient Descent (PGD) to generate adversarial examples during training, effectively solving a min-max optimization problem. Feature squeezing techniques like spatial smoothing (median filtering) or bit-depth reduction (e.g., from 8-bit to 4-bit) can be applied as a preprocessing step, but must be carefully tuned to avoid degrading accuracy on legitimate inputs. In practice, combining adversarial training with feature squeezing provides a layered defense, as each method addresses different aspects of the attack surface.
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: Train the model with adversarial examples (adversarial training) — 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.
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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