- A
Data poisoning
Why wrong: Data poisoning corrupts training data, not inference-time inputs.
- B
Model inversion
Why wrong: Model inversion reconstructs training data from model outputs.
- C
Membership inference
Why wrong: Membership inference determines if a record was in the training set.
- D
Adversarial example
Adversarial examples are inputs with imperceptible perturbations that cause misclassification.
Question 618 of 1,000
AI0-001 AI Security Practice Question
This AI0-001 practice question tests your understanding of ai security. 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.
A security analyst is investigating a potential adversarial attack on a production image classifier. The attack involves tiny perturbations that are invisible to the human eye but cause the model to misclassify a stop sign as a speed limit sign. Which type of attack is this?
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 example
This is an adversarial example attack, where imperceptible perturbations are added to the input (e.g., a stop sign) to cause the model to misclassify it (e.g., as a speed limit sign). The perturbations are crafted using gradient-based methods (like FGSM or PGD) to maximize the model's loss, exploiting its linearity in high-dimensional spaces. This differs from other attacks because it targets the inference phase, not the training data or model parameters.
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.
- ✗
Data poisoning
Why it's wrong here
Data poisoning corrupts training data, not inference-time inputs.
- ✗
Model inversion
Why it's wrong here
Model inversion reconstructs training data from model outputs.
- ✗
Membership inference
Why it's wrong here
Membership inference determines if a record was in the training set.
- ✓
Adversarial example
Why this is correct
Adversarial examples are inputs with imperceptible perturbations that cause misclassification.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between attacks that occur during training (e.g., data poisoning) versus inference (e.g., adversarial examples), and candidates mistakenly choose data poisoning because they confuse 'adding noise to input' with 'corrupting training data'.
Trap categories for this question
Command / output trap
Model inversion reconstructs training data from model outputs.
Detailed technical explanation
How to think about this question
Adversarial examples exploit the model's decision boundary by adding small, carefully calculated noise—often bounded by an L-infinity norm (e.g., epsilon = 0.01 for pixel values 0–255)—to push the input across the boundary. In real-world scenarios, physical adversarial patches (e.g., printed stickers on a stop sign) can fool models even under varying lighting and angles, as demonstrated in research on robust physical perturbations. This attack is a key focus of AI security because it reveals the brittleness of deep neural networks to inputs that are semantically identical to humans.
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 — This question tests AI Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Adversarial example — This is an adversarial example attack, where imperceptible perturbations are added to the input (e.g., a stop sign) to cause the model to misclassify it (e.g., as a speed limit sign). The perturbations are crafted using gradient-based methods (like FGSM or PGD) to maximize the model's loss, exploiting its linearity in high-dimensional spaces. This differs from other attacks because it targets the inference phase, not the training data or model parameters.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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