- A
Model extraction
Why wrong: Model extraction aims to steal the model via queries, not to manipulate its outputs.
- B
Prompt injection
Why wrong: Prompt injection targets LLMs via text prompts, not traditional ML models.
- C
Adversarial example
Adversarial examples are crafted to fool a model during inference by small perturbations.
- D
Data poisoning
Why wrong: Data poisoning corrupts training data, not inference-time inputs.
Question 510 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.
An organization deploys a machine learning model for credit scoring. An attacker submits carefully crafted loan applications that are slightly outside normal ranges but cause the model to approve high-risk loans. What 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 the attacker crafts inputs with small, carefully chosen perturbations that cause the ML model to misclassify them. In credit scoring, submitting loan applications with values slightly outside normal ranges exploits the model's decision boundary to approve high-risk loans, a classic evasion technique.
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.
- ✗
Model extraction
Why it's wrong here
Model extraction aims to steal the model via queries, not to manipulate its outputs.
- ✗
Prompt injection
Why it's wrong here
Prompt injection targets LLMs via text prompts, not traditional ML models.
- ✓
Adversarial example
Why this is correct
Adversarial examples are crafted to fool a model during inference by small perturbations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data poisoning
Why it's wrong here
Data poisoning corrupts training data, not inference-time inputs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between data poisoning (training-time attack) and adversarial examples (inference-time attack), so candidates mistakenly choose data poisoning when they see 'crafted inputs' without recognizing the attack occurs after deployment.
Trap categories for this question
Command / output trap
Model extraction aims to steal the model via queries, not to manipulate its outputs.
Detailed technical explanation
How to think about this question
Adversarial examples exploit the linearity of high-dimensional spaces in neural networks; small perturbations in input features can push the sample across the decision boundary. In credit scoring, features like income or debt-to-income ratio are perturbed by a few percent, which is imperceptible to humans but sufficient to flip the model's output from 'deny' to 'approve'. Real-world attacks have been demonstrated against lending models using gradient-based methods like Fast Gradient Sign Method (FGSM) or Projected Gradient Descent (PGD).
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 the attacker crafts inputs with small, carefully chosen perturbations that cause the ML model to misclassify them. In credit scoring, submitting loan applications with values slightly outside normal ranges exploits the model's decision boundary to approve high-risk loans, a classic evasion technique.
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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