AI0-001 AI Security Practice Question
Which privacy-preserving technique allows a model to be trained across decentralized data sources without the raw data ever leaving each source?
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 locally on each device or server and only shares model updates, preserving data locality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Homomorphic encryption
Why it's wrong here
Homomorphic encryption allows computation on encrypted data but is not specifically a decentralized training approach.
- ✗
Secure multi-party computation
Why it's wrong here
Secure multi-party computation enables joint computation without revealing inputs but is not the standard decentralized training method.
- ✗
Differential privacy
Why it's wrong here
Differential privacy adds noise to protect individual records but does not keep data decentralized.
- ✓
Federated learning
Why this is correct
Correct. Federated learning trains across decentralized data without raw data sharing.
About these practice questions
This AI0-001 question is part of Courseiva's 754-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.