AI0-001 AI Governance and Ethics Practice Question
A hospital wants to train a diagnostic model using patient data from multiple hospitals without sharing raw patient records. Which technique enables collaborative model training while keeping data decentralised?
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 models across decentralised data without exchanging raw data. Differential privacy adds noise. Anonymisation removes identifiers. Pseudonymisation replaces identifiers with pseudonyms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pseudonymisation
Why it's wrong here
Pseudonymisation replaces identifiers but does not allow training without data leaving the hospital.
- ✗
Differential privacy
Why it's wrong here
Differential privacy protects individual records by adding noise, but it does not enable collaborative training without sharing data.
- ✓
Federated learning
Why this is correct
Federated learning trains a shared model by aggregating only model updates, never raw patient data.
- ✗
Anonymisation
Why it's wrong here
Anonymisation removes identifiers but still requires centralising data for training.
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.