- A
Differential privacy
Differential privacy adds calibrated noise during training to bound the influence of any single data point.
- B
Homomorphic encryption
Why wrong: Homomorphic encryption allows computation on encrypted data but does not prevent the model from memorizing training data.
- C
Data sanitization
Why wrong: Data sanitization removes or masks sensitive data before training but does not provide a mathematical privacy guarantee like DP.
- D
Federated learning
Why wrong: Federated learning trains across decentralized data but does not inherently add noise to prevent memorization.
Question 235 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 AI team is concerned about their model leaking sensitive information from its training data when queried. Which privacy-preserving technique adds noise to the training process to limit what can be inferred about any individual record?
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 (A) is the correct answer because it directly addresses the concern of leaking sensitive information from training data by adding calibrated noise to the training process or query responses. This noise ensures that the output of the model does not significantly change whether any single individual's record is included or excluded, thereby limiting what can be inferred about any specific record. The technique is formalized through a privacy budget (ε, epsilon) that quantifies the privacy guarantee, making it the standard approach for privacy-preserving machine learning.
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 adds calibrated noise during training to bound the influence of any single data point.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Homomorphic encryption
Why it's wrong here
Homomorphic encryption allows computation on encrypted data but does not prevent the model from memorizing training data.
- ✗
Data sanitization
Why it's wrong here
Data sanitization removes or masks sensitive data before training but does not provide a mathematical privacy guarantee like DP.
- ✗
Federated learning
Why it's wrong here
Federated learning trains across decentralized data but does not inherently add noise to prevent memorization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between techniques that protect data during computation (like homomorphic encryption) versus those that protect against inference from model outputs (like differential privacy), causing candidates to confuse encryption with privacy guarantees.
Detailed technical explanation
How to think about this question
Under the hood, differential privacy often uses mechanisms like the Laplace or Gaussian mechanism to inject noise calibrated to the sensitivity of the query (the maximum change in output due to a single record). In deep learning, this is implemented via differentially private stochastic gradient descent (DP-SGD), where gradients are clipped and noise is added before updating the model. A subtle behavior is that the privacy budget (ε) must be carefully managed; even with small noise, repeated queries can accumulate and erode the privacy guarantee, a concept known as composition.
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 (A) is the correct answer because it directly addresses the concern of leaking sensitive information from training data by adding calibrated noise to the training process or query responses. This noise ensures that the output of the model does not significantly change whether any single individual's record is included or excluded, thereby limiting what can be inferred about any specific record. The technique is formalized through a privacy budget (ε, epsilon) that quantifies the privacy guarantee, making it the standard approach for privacy-preserving machine learning.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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