AI0-001 AI Governance and Ethics Practice Question
A company wants to train a language model on sensitive customer data without transferring the raw data to a central server. Which privacy-preserving technique should they use?
⚠ Common exam trap
CompTIA AI often tests the distinction between techniques that prevent raw data transfer (federated learning) versus techniques that protect data after it has been transferred (differential privacy, anonymisation), leading candidates to confuse privacy-preserving computation with output 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 is the correct technique because it trains a shared model across decentralized edge devices holding local data, without transferring raw customer data to a central server. Only model updates (gradients) are sent to the aggregation server, preserving data locality and reducing exposure. This directly addresses the requirement of avoiding raw data transfer while still enabling collaborative model 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.
- ✓
Federated learning
Why this is correct
Federated learning trains the model locally on each device, exchanging only model updates rather than raw records, so sensitive customer data never leaves its source. This directly satisfies the stem's constraint of avoiding transfer to a central server, unlike centralised training approaches that require aggregating the dataset first.
- ✗
Differential privacy
Why it's wrong here
Differential privacy adds calibrated noise to outputs or gradients, but training still requires data or updates to reach a central aggregator, so raw records leave the source. It suits publishing statistics or query results with formal privacy guarantees, not keeping training data on-device.
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Data minimisation
Why it's wrong here
Data minimisation reduces how much personal data is collected or retained, yet the remaining records must still be transferred to the training server. It suits limiting collection scope under privacy principles, not enabling local training where raw data never leaves its origin.
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Anonymisation
Why it's wrong here
Anonymisation strips or masks identifiers before processing, so the raw records still leave each site for central training; it cannot keep data local. It suits publishing or sharing datasets where re-identification risk is acceptable, not federated training that never moves raw data.
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 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.