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AI0-001 AI Security Practice Question

An organization wants to train a machine learning model on sensitive patient data without exposing individual records. Which privacy-preserving technique allows the model to learn from data distributed across multiple hospitals without raw data leaving each site?

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 at each site and only shares model updates, not raw data. Differential privacy adds noise, homomorphic encryption allows computation on encrypted data, and k-anonymity is a data anonymization technique.

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 computationally intensive and not typically used for distributed training across sites.

  • Federated learning

    Why this is correct

    Federated learning trains on decentralized data and only shares model weights, preserving privacy.

  • k-anonymity

    Why it's wrong here

    k-anonymity anonymizes datasets but requires data centralization.

  • Differential privacy

    Why it's wrong here

    Differential privacy adds noise to protect individual data but data still leaves each site.

About these practice questions

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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.