- A
Model extraction
Model extraction aims to steal the model itself by analyzing query responses.
- B
Data poisoning
Why wrong: Data poisoning corrupts training data, not inference queries.
- C
Membership inference
Why wrong: Membership inference determines if a data point was in the training set.
- D
Model inversion
Why wrong: Model inversion reconstructs training data, not model parameters.
Question 709 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 attacker repeatedly queries a public LLM API with carefully crafted inputs to reconstruct the model's architecture and approximate weights. This is an example of which attack?
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
✓
Model extraction
Model extraction attacks involve querying a public API with carefully crafted inputs to reconstruct a target model's architecture and approximate weights. By analyzing the outputs (e.g., logits or probabilities), an attacker can train a substitute model that mimics the original, enabling offline exploitation or competitive intelligence. This directly matches the scenario described.
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 this is correct
Model extraction aims to steal the model itself by analyzing query responses.
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 queries.
- ✗
Membership inference
Why it's wrong here
Membership inference determines if a data point was in the training set.
- ✗
Model inversion
Why it's wrong here
Model inversion reconstructs training data, not model parameters.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between model extraction (stealing the model) and model inversion (reconstructing training data), so the trap here is confusing 'reconstructing the model's architecture and weights' with 'reconstructing training samples' from model outputs.
Detailed technical explanation
How to think about this question
Under the hood, model extraction exploits the API's output fidelity—e.g., returning softmax probabilities or logits—to train a student model via knowledge distillation. A real-world example is the 2016 attack on BigML and Amazon ML, where attackers used hundreds of thousands of queries to steal decision tree models. The attack's success depends on the API's rate limits and output granularity; returning only top-1 labels makes extraction harder but not impossible.
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: Model extraction — Model extraction attacks involve querying a public API with carefully crafted inputs to reconstruct a target model's architecture and approximate weights. By analyzing the outputs (e.g., logits or probabilities), an attacker can train a substitute model that mimics the original, enabling offline exploitation or competitive intelligence. This directly matches the scenario described.
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.
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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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