AI0-001 AI Security Practice Question
A healthcare AI system uses patient data to predict disease risk. To comply with privacy regulations, the organization wants to ensure that the model cannot reveal whether a specific patient's data was used in training. Which technique should they implement?
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 adds noise to the training process, making it difficult to determine if any individual was in the training set. This directly addresses membership inference attacks.
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 ensures that the inclusion or exclusion of any single record has a limited effect on the model, protecting against membership inference.
- ✗
Homomorphic encryption
Why it's wrong here
Homomorphic encryption allows computation on encrypted data but does not directly address membership inference.
- ✗
Federated learning
Why it's wrong here
Federated learning keeps data decentralized but does not prevent membership inference on the aggregated model.
- ✗
Model validation
Why it's wrong here
Model validation assesses performance, not privacy protection against membership inference.
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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.