AI0-001 AI Security Practice Question
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?
⚠ Common exam trap
The AI0-001 exam 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.
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.
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.
- ✗
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.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.