AI0-001 AI Governance and Ethics Practice Question
A hospital wants to train a diagnostic model using data from multiple hospitals without sharing raw patient data. Which technique allows model training across decentralised data while preserving 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
✓
Federated learning
Federated learning trains a shared model by aggregating updates from local data without moving the data itself, preserving privacy. Differential privacy adds noise but doesn't decentralise data. Data centralisation violates privacy. Anonymisation alone doesn't allow collaborative training.
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 applied to the combined dataset
Why it's wrong here
Differential privacy requires centralising data first, which violates the privacy requirement of not sharing raw data.
- ✗
Centralising all data in one location and anonymising it
Why it's wrong here
Centralising data exposes raw patient data, even if anonymised, and may not be allowed under privacy regulations.
- ✓
Federated learning
Why this is correct
Federated learning enables collaborative model training without sharing raw data, keeping data at each hospital.
- ✗
Using pseudonymisation and then pooling the data
Why it's wrong here
Pseudonymisation is reversible and pooling data still shares identifiable information.
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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.