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?
⚠ Common exam trap
AI0-001 often tests the confusion between privacy-preserving techniques (pseudonymisation, anonymisation, differential privacy) and collaborative training paradigms (federated learning), so candidates may pick a privacy method that does not enable decentralised model training.
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 enables multiple parties to collaboratively train a shared model without exchanging raw data. Each hospital trains a local model on its own patient records, and only model updates (e.g., gradients or weights) are sent to a central server for aggregation. This keeps sensitive data decentralised and reduces privacy risks, making it the correct choice for collaborative training across hospitals.
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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Pseudonymisation
Why it's wrong here
Pseudonymisation replaces direct identifiers with tokens, but the records still leave their source and can be re-linked via the token mapping, so raw data is effectively shared. It is tempting as a de-identification step, yet the scenario demands that records never leave each hospital, which federated learning provides.
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Differential privacy
Why it's wrong here
Differential privacy adds noise to outputs or gradients to protect individual records, but it does not itself coordinate training across sites holding separate datasets. It is tempting because it preserves privacy in published results, yet the scenario requires a federated approach where each hospital trains locally and only model updates are shared.
- ✓
Federated learning
Why this is correct
Federated learning trains a shared model across hospitals by exchanging only model updates or gradients, keeping raw patient records on each local site. This decentralised approach satisfies the requirement to collaborate without sharing patient data, unlike centralised training on pooled records.
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Anonymisation
Why it's wrong here
Anonymisation strips identifying attributes from records, but the data must still be centralised for training, contradicting the requirement that records remain at each hospital. It is tempting because it removes identifiers, yet it addresses disclosure risk rather than enabling decentralised collaborative training.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.