The answer is an adversarial example attack. This is correct because the technique involves adding small, often imperceptible perturbations to legitimate input data—in this case, an image of a stop sign—specifically designed to cause a machine learning model to misclassify the output, such as identifying it as a speed limit sign. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of evasion attacks within adversarial machine learning, a key domain of AI security. A common trap is confusing this with a poisoning attack, which alters the training data itself rather than the input at inference time. To remember, think of the word "evade": the attacker evades the model's correct classification by subtly altering the input after the model is already trained.
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.
A security researcher demonstrates that by adding small perturbations to an image of a stop sign, an autonomous vehicle's AI misclassifies it as a speed limit sign. This is an example of which type of attack?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Adversarial example attack
Adding small perturbations to input to cause misclassification is a classic adversarial example attack, which falls under evasion attacks (adversarial machine learning). Poisoning alters training data, extraction steals model parameters, and inference determines membership.
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 attack
Why it's wrong here
Data poisoning alters training data, not inputs at inference time.
✗
Model extraction attack
Why it's wrong here
Model extraction aims to steal the model architecture/parameters, not cause misclassification.
✓
Adversarial example attack
Why this is correct
Adversarial examples are crafted inputs with perturbations that fool the model.
Related concept
Read the scenario before looking for a memorised answer.
✗
Membership inference attack
Why it's wrong here
Membership inference determines if data was in training set, not misclassification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 example attack — Adding small perturbations to input to cause misclassification is a classic adversarial example attack, which falls under evasion attacks (adversarial machine learning). Poisoning alters training data, extraction steals model parameters, and inference determines membership.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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 are common types of adversarial attacks on AI models?
easy
A.Hyperparameter tuning
B.Transfer learning
✓ C.Evasion attack
D.Backdoor attack
✓ E.Data poisoning
Why C: Evasion attacks and data poisoning are well-known adversarial attack vectors.
Last reviewed: Jun 23, 2026
Question Discussion
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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.
Question Discussion
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