- A
Differential privacy
Differential privacy provides a formal mathematical guarantee (epsilon) that the presence or absence of any single record in the training set cannot be inferred from model outputs.
- B
Federated learning
Why wrong: Federated learning keeps data on local devices, but model updates can still leak information; it does not provide mathematical privacy guarantees for query responses.
- C
k-anonymity
Why wrong: k-anonymity is a data anonymization technique for datasets, not for protecting model outputs or training data during queries.
- D
Homomorphic encryption
Why wrong: Homomorphic encryption enables computation on encrypted data, but it is computationally expensive and does not directly prevent inference attacks on model outputs.
Question 53 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.
A machine learning team is developing a model to predict loan defaults using sensitive customer financial data. They need to share the model with third-party auditors without exposing individual customer records. Which privacy-preserving technique allows auditors to query the model while providing mathematical guarantees about the privacy of the training data?
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
✓
Differential privacy
Differential privacy is correct because it adds calibrated noise to the model's training process or query responses, providing a formal mathematical guarantee (ε-differential privacy) that the inclusion or exclusion of any single individual's data does not significantly affect the output. This allows auditors to query the model without exposing individual customer records, as the noise bounds the information leakage from the training data.
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.
- ✓
Differential privacy
Why this is correct
Differential privacy provides a formal mathematical guarantee (epsilon) that the presence or absence of any single record in the training set cannot be inferred from model outputs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Federated learning
Why it's wrong here
Federated learning keeps data on local devices, but model updates can still leak information; it does not provide mathematical privacy guarantees for query responses.
- ✗
k-anonymity
Why it's wrong here
k-anonymity is a data anonymization technique for datasets, not for protecting model outputs or training data during queries.
- ✗
Homomorphic encryption
Why it's wrong here
Homomorphic encryption enables computation on encrypted data, but it is computationally expensive and does not directly prevent inference attacks on model outputs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that federated learning inherently provides privacy guarantees, when in fact it only addresses data locality and does not prevent model inversion or membership inference attacks without additional differential privacy mechanisms.
Trap categories for this question
Command / output trap
k-anonymity is a data anonymization technique for datasets, not for protecting model outputs or training data during queries.
Detailed technical explanation
How to think about this question
Differential privacy works by adding noise drawn from a Laplace or Gaussian distribution to the model's gradients or query results, calibrated to the sensitivity of the function (the maximum change in output due to a single record). In practice, a common approach is to train the model with DP-SGD (Differentially Private Stochastic Gradient Descent), where gradients are clipped and noise is added before updating weights, ensuring that the final model parameters do not memorize individual training examples. A real-world scenario is Apple's use of local differential privacy to collect user usage statistics without identifying individuals.
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: Differential privacy — Differential privacy is correct because it adds calibrated noise to the model's training process or query responses, providing a formal mathematical guarantee (ε-differential privacy) that the inclusion or exclusion of any single individual's data does not significantly affect the output. This allows auditors to query the model without exposing individual customer records, as the noise bounds the information leakage from the training data.
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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