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?
⚠ Common exam trap
AI0-001 often tests the confusion between privacy-preserving techniques that protect data in transit or at rest (homomorphic encryption, federated learning) and those that provide a formal guarantee against membership inference (differential privacy).
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 is the correct technique because it adds calibrated statistical noise (e.g., via the Laplace or Gaussian mechanism) to query results or gradients so that the inclusion or exclusion of any single patient's record produces a nearly indistinguishable output. This provides a formal, mathematically provable guarantee against membership inference, which is exactly the requirement stated. Homomorphic encryption, federated learning, and model validation address different concerns (computation on encrypted data, decentralized training, and general model quality) and do not by themselves prevent membership disclosure.
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 to query outputs or training gradients, bounding any single patient's influence so an adversary cannot infer membership. This directly satisfies the requirement that the model must not reveal whether a specific patient's data was used in training.
- ✗
Homomorphic encryption
Why it's wrong here
Homomorphic encryption allows computation on encrypted data, protecting data in use, but it does not prevent a trained model from revealing whether a record was in its training set. Differential privacy addresses membership inference. Homomorphic encryption would be correct when computations must occur on ciphertext without exposing plaintext to the processing system.
- ✗
Federated learning
Why it's wrong here
Federated learning trains models across decentralised data without centralising it, but the resulting model can still leak training-set membership through inference. Membership inference requires differential privacy or machine unlearning. Federated learning suits scenarios where raw patient data must remain on local devices or hospital premises during training.
- ✗
Model validation
Why it's wrong here
Model validation assesses accuracy, robustness, and generalisation; it does not bound what an attacker can infer about individual training records. Differential privacy provides that guarantee. Model validation would be the right choice when confirming a model meets performance and fairness criteria before deployment, not when preventing training-data membership disclosure.
About these practice questions
This AI0-001 question is part of Courseiva's 962-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.