AI0-001 AI Security, Ethics and Governance Practice Question
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?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between attacks that occur during training (poisoning) versus inference (adversarial examples), so candidates mistakenly choose data poisoning when the scenario clearly describes input manipulation at test time.
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 attack
This is an adversarial example attack because the researcher adds imperceptible perturbations to the input image (the stop sign) to cause the AI model to output an incorrect classification (speed limit sign). Adversarial examples exploit the model's sensitivity to small, crafted changes in input data, leading to misclassification without altering the underlying task or training data.
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.
- ✗
Membership inference attack
Why it's wrong here
A membership inference attack determines whether a specific data record was part of the model’s training set, not whether input perturbations alter classification output. It fails here because the scenario involves manipulating an image to cause misclassification, not inferring training data membership. This option is tempting because both involve adversarial manipulation of model behaviour, but membership inference targets data privacy, not input integrity. It would be correct if the question asked about extracting whether a particular stop-sign image was used during training.
About these practice questions
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Same concept, more angles
1 more way 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 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 (Option C) are a common type of adversarial attack where an attacker crafts malicious input data that is intentionally designed to cause a trained AI model to make incorrect predictions or classifications, often by adding imperceptible perturbations to legitimate inputs. This exploits the model's sensitivity to small changes in feature space, leading to misclassification without altering the model itself.
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.