AI0-001 AI Governance and Ethics Practice Question
A hospital is implementing an AI system to analyze patient X-rays for potential fractures. The hospital must comply with HIPAA regulations. Which privacy-preserving technique allows the model to be trained on data from multiple hospitals without sharing raw patient data?
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
✓
Federated learning
Federated learning trains model copies across multiple sites and aggregates only model updates (gradients) to a central server, keeping patient data local. Differential privacy adds noise to data but does not inherently prevent data sharing. Anonymisation and pseudonymisation still require sharing data, albeit de-identified.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Federated learning
Why this is correct
Federated learning trains models locally and only shares model parameters, not raw patient data, thus preserving privacy across hospitals.
- ✗
Differential privacy
Why it's wrong here
Differential privacy protects individual privacy by adding noise, but the data is still shared centrally; it does not prevent sharing of raw data.
- ✗
Data anonymisation
Why it's wrong here
Anonymisation removes identifiers, but the data is still shared with a central server, which may not fully satisfy HIPAA data sharing restrictions.
- ✗
Data pseudonymisation
Why it's wrong here
Pseudonymisation replaces identifiers with pseudonyms, but the data is still transmitted centrally, and re-identification risks remain.
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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.