AI0-001 AI Security Practice Question
A data science team wants to train a model on sensitive medical records while minimizing the risk of leaking individual patient information. They need to ensure that the model's outputs do not reveal whether a specific patient's data was used in training. Which privacy-preserving technique directly addresses this requirement?
⚠ Common exam trap
CompTIA often tests the misconception that data anonymization is sufficient for preventing membership inference, when in fact it does not provide a formal mathematical guarantee against linkage or re-identification attacks.
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 directly addresses the requirement by adding calibrated noise to the training process or model outputs, ensuring that the inclusion or exclusion of any single patient's data does not significantly affect the final model. This provides a formal mathematical guarantee (ε-differential privacy) that an adversary cannot infer whether a specific individual's records were used, even with auxiliary information.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Homomorphic encryption
Why it's wrong here
Homomorphic encryption allows computation on encrypted data but does not prevent membership inference, since outputs can still reveal whether a record was in the training set; differential noise addition addresses that. It tempts because it protects data during processing, and it would be correct where computation must occur without decrypting the data.
- ✓
Differential privacy
Why this is correct
Differential privacy adds calibrated noise to queries or training so that any single patient's inclusion cannot be distinguished in the output, directly satisfying the requirement that outputs not reveal whether a specific patient's data was used.
- ✗
Data anonymization
Why it's wrong here
Anonymisation strips or masks direct identifiers before training, but a model can still memorise and leak membership through its outputs; it does not bound that leakage. It is tempting because it is the standard first step for handling personal data, and it is the right choice when identifiers must simply be removed.
- ✗
Federated learning
Why it's wrong here
Federated learning keeps training data on distributed clients and shares only model updates, so it never provides the formal guarantee that an output cannot be linked to a training record. It is tempting because it genuinely reduces raw-data movement across silos, which is its real purpose.
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.