AI0-001 AI Governance and Ethics Practice Question
A data scientist is using differential privacy to protect individual privacy in a training dataset. Which TWO actions are correct implementations of differential privacy?
⚠ Common exam trap
A common pitfall in this question is thinking that removing PII or aggregating data is sufficient for differential privacy. In reality, differential privacy requires a formal mathematical framework with noise addition and a privacy budget parameter. CompTIA often tests this distinction.
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
✓
Set a privacy budget (epsilon) to limit information leakage
Setting a privacy budget (epsilon) is a core mechanism in differential privacy that quantifies and limits the amount of information leaked about any individual in the dataset. By controlling epsilon, the data scientist can formally bound the privacy loss, ensuring that the model's outputs do not reveal whether any specific individual's data was included in 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.
- ✗
Train the model on a small subset of data to reduce exposure
Why it's wrong here
Training on a subset may reduce accuracy but does not provide formal differential privacy guarantees.
- ✗
Remove all personally identifiable information (PII) from the dataset
Why it's wrong here
Removing PII is an anonymization technique, not differential privacy.
- ✗
Aggregate data into groups before training
Why it's wrong here
Aggregation can reduce granularity but does not provide differential privacy guarantees.
- ✓
Set a privacy budget (epsilon) to limit information leakage
Why this is correct
The privacy budget epsilon quantifies the privacy guarantee and is a core concept of differential privacy.
- ✓
Add noise to the training data to mask individual contributions
Why this is correct
Adding noise to the data or gradients is a standard method to achieve differential privacy.
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.