- A
Federated learning
Why wrong: Federated learning keeps data decentralized but does not prevent memorization of local data patterns.
- B
Homomorphic encryption
Why wrong: Homomorphic encryption allows computation on encrypted data but does not prevent memorization during training.
- C
Differential privacy
Differential privacy mathematically limits what can be inferred about individuals from the model.
- D
Data anonymization
Why wrong: Anonymization removes identifiers but does not guarantee protection against inference attacks on model outputs.
Question 81 of 1,000
AI0-001 AI Security Practice Question
This AI0-001 practice question tests your understanding of ai security. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist wants to protect the privacy of individuals whose data is used to train a model, even if the model is compromised. Which technique ensures that the model does not memorize sensitive information?
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 (C) is the correct technique because it adds calibrated noise to the training data or model updates, ensuring that the model's outputs do not reveal whether any specific individual's data was included. This guarantees that even if an attacker gains full access to the model, they cannot extract sensitive information about any single record, as the noise bounds the influence of any one data point.
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.
- ✗
Federated learning
Why it's wrong here
Federated learning keeps data decentralized but does not prevent memorization of local data patterns.
- ✗
Homomorphic encryption
Why it's wrong here
Homomorphic encryption allows computation on encrypted data but does not prevent memorization during training.
- ✓
Differential privacy
Why this is correct
Differential privacy mathematically limits what can be inferred about individuals from the model.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data anonymization
Why it's wrong here
Anonymization removes identifiers but does not guarantee protection against inference attacks on model outputs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that data anonymization (D) is sufficient for model privacy, but candidates must recognize that anonymization does not protect against model inversion or membership inference attacks, whereas differential privacy provides a formal mathematical guarantee.
Trap categories for this question
Command / output trap
Anonymization removes identifiers but does not guarantee protection against inference attacks on model outputs.
Detailed technical explanation
How to think about this question
Differential privacy typically uses mechanisms like the Laplace or Gaussian mechanism to add noise proportional to the sensitivity of the query (e.g., the maximum change one data point can cause). In deep learning, this is often implemented via DP-SGD (Differentially Private Stochastic Gradient Descent), where gradients are clipped and noise is added before aggregation, bounding the privacy loss with a parameter ε (epsilon). A real-world scenario is Apple's use of local differential privacy in iOS to collect usage statistics without exposing individual user behavior.
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 (C) is the correct technique because it adds calibrated noise to the training data or model updates, ensuring that the model's outputs do not reveal whether any specific individual's data was included. This guarantees that even if an attacker gains full access to the model, they cannot extract sensitive information about any single record, as the noise bounds the influence of any one data point.
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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