AI0-001 AI Security Practice Question
An AI security analyst is evaluating a model that classifies images. The team wants to test whether small, imperceptible changes to input images can cause misclassification. Which type of attack are they testing?
⚠ Common exam trap
A common mix-up: candidates confuse adversarial examples with data poisoning, but the key distinction is that adversarial examples occur at inference time with small input perturbations, while data poisoning corrupts the training data during the learning phase.
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 examples
Adversarial examples are specifically crafted inputs with small, imperceptible perturbations designed to cause a machine learning model to misclassify them. This directly matches the scenario of testing whether tiny changes to images can fool the classifier, which is a core concept in AI security for evaluating model robustness.
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 the training set so the learned model behaves wrongly, which happens before deployment, not through crafted test inputs. It is tempting because both attacks target model integrity, but poisoning alters training data whereas the scenario describes perturbing inference-time images.
- ✓
Adversarial examples
Why this is correct
Adversarial examples are inputs deliberately perturbed by small, often imperceptible amounts that exploit the model's learned decision boundaries, causing misclassification. This matches the team's goal of testing whether tiny image changes flip the predicted class.
- ✗
Model inversion
Why it's wrong here
Model inversion reconstructs representative training inputs from outputs, exposing sensitive features rather than causing misclassification. It is tempting because it also manipulates queries, but its goal is extracting training data, whereas the scenario tests adversarial perturbations that flip predicted labels.
- ✗
Membership inference
Why it's wrong here
Membership inference determines whether a specific record was in the training set, revealing privacy leakage rather than forcing misclassification. It is tempting because it also probes model behaviour, but it queries confidence outputs about training membership, not imperceptible pixel perturbations.
About these practice questions
This AI0-001 question is part of Courseiva's 962-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.