AI0-001 AI Governance and Ethics Practice Question
Which technique adds controlled noise to query results or training data to prevent an attacker from inferring whether a specific individual's data was included in the dataset?
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
✓
Differential privacy
Differential privacy adds calibrated noise to ensure the output does not reveal individual participation. Anonymisation removes identifiers. Pseudonymisation replaces identifiers. Federated learning decentralises data but does not necessarily add noise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Anonymisation
Why it's wrong here
Anonymisation removes personally identifiable information but does not add noise to protect against inference attacks.
- ✗
Federated learning
Why it's wrong here
Federated learning keeps data decentralised but does not inherently add noise; it can be combined with differential privacy.
- ✓
Differential privacy
Why this is correct
Differential privacy injects noise into computations or outputs to bound the risk of re-identification.
- ✗
Pseudonymisation
Why it's wrong here
Pseudonymisation replaces identifiers with pseudonyms, but linkage may still be possible.
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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.