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 injects calibrated noise during training, bounding any single record's influence on the model's output. This mathematically limits what an attacker can infer about an individual training record from queries, satisfying the stated privacy requirement.
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Homomorphic encryption
Why it's wrong here
Homomorphic encryption lets computation run on encrypted data, protecting data in use, but it neither injects noise into training nor limits inference about a training record. It would be correct when a party must process data without decrypting it, such as outsourced computation.
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Data sanitization
Why it's wrong here
Data sanitization removes or masks sensitive fields from datasets before training, so it cannot add noise during training and gives no formal bound on what can be inferred about an individual record. It is the right choice when source data must be cleaned of direct identifiers prior to use.
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Federated learning
Why it's wrong here
Federated learning keeps training data on local devices and shares only model updates, so no noise is added to the training process itself and inference about an individual record is not bounded. It is correct when data cannot be centralised, such as across hospitals or handsets.
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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.