AI0-001 AI Governance and Ethics Practice Question
A hospital wants to train a diagnostic AI model using data from multiple hospitals without sharing raw patient data. Which privacy-preserving technique allows collaborative training while keeping data local?
⚠ Common exam trap
CompTIA emphasizes the distinction between techniques that alter data before sharing (e.g., anonymization, pseudonymization) and techniques that keep data local and share only model parameters (federated learning). The trap is assuming that anonymizing or pseudonymizing data satisfies the 'keep data local' requirement, but these still involve data leaving the hospital.
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 is the correct technique because it enables multiple hospitals to collaboratively train a shared diagnostic AI model without exchanging raw patient data. Instead, each hospital trains a local model on its own data, and only encrypted model updates (e.g., gradients or weights) are sent to a central server for aggregation. This keeps all sensitive patient information local, directly addressing the requirement of data locality while still benefiting from collective learning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Differential privacy
Why it's wrong here
Differential privacy adds noise to outputs or gradients to protect individuals, yet it does not itself keep training data local across sites; it is a statistical guarantee, not a distributed training architecture. It tempts because it is a recognised privacy technique, and would suit publishing aggregate statistics or query results.
- ✓
Federated learning
Why this is correct
Federated learning trains a shared model across hospitals by exchanging only model updates, such as gradients or weights, rather than raw patient records. Each hospital's data therefore stays local, satisfying the requirement for collaborative training without sharing patient data.
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Data anonymisation
Why it's wrong here
Anonymisation strips identifying details before data is transferred, so records still move to a central location rather than staying at each hospital. It tempts because it removes direct identifiers, and would suit releasing a dataset publicly where re-identification risk is acceptably low and central pooling is permitted.
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Data pseudonymisation
Why it's wrong here
Pseudonymisation replaces identifiers with tokens but still requires pooling the altered records centrally, so raw data leaves each hospital. It tempts because it reduces re-identification risk during analysis, and would suit sharing a dataset for research where a trusted central repository is acceptable.
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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.