AI0-001 AI Security, Ethics and Governance Practice Question
Which TWO techniques are specifically designed to protect individual privacy when training AI models?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between techniques that improve model performance (dropout, pruning, regularization) and those that explicitly safeguard privacy, leading candidates to confuse regularization with privacy protection.
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 (C) is a technique that adds calibrated noise to training data or model updates, ensuring that the output of the model does not reveal whether any specific individual's data was included. This provides a formal mathematical guarantee of privacy, quantified by the epsilon parameter, making it a direct privacy-preserving method for AI 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.
- ✗
Dropout
Why it's wrong here
Dropout is a regularization technique to prevent overfitting, not privacy.
- ✗
Model pruning
Why it's wrong here
Model pruning reduces model size but does not enhance privacy.
- ✓
Differential privacy
Why this is correct
Differential privacy adds noise to training to prevent data leakage.
- ✗
Regularization
Why it's wrong here
Regularization controls model complexity, not privacy.
- ✓
Anonymization
Why this is correct
Anonymization removes personally identifiable information from training data.
About these practice questions
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Same concept, more angles
2 more ways this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A healthcare organization uses an AI model to predict patient readmission risk. To comply with patient privacy regulations, they apply differential privacy during training. What is the primary trade-off of using differential privacy?
medium- A.Increased training time for reduced bias
- B.Lower interpretability for higher fairness
- C.Faster inference for lower memory usage
- ✓ D.Reduced model accuracy for increased privacy
Why D: Differential privacy works by adding calibrated noise to the training process or model outputs, which directly reduces the model's accuracy in exchange for a quantifiable privacy guarantee (e.g., ε-differential privacy). This trade-off is fundamental: stronger privacy (lower ε) requires more noise, which degrades predictive performance. The healthcare organization must balance the need to protect patient data against the clinical utility of accurate readmission predictions.
Variation 2. A research lab trains a language model using DP-SGD. What primary privacy risk does this technique mitigate?
hard- A.Data poisoning attacks
- ✓ B.Membership inference attacks
- C.Adversarial patch attacks
- D.Model inversion attacks
Why B: DP-SGD (Differentially Private Stochastic Gradient Descent) mitigates membership inference attacks by adding calibrated noise to gradients during training, which bounds the influence any single training example can have on the final model. This differential privacy guarantee makes it difficult for an adversary to determine whether a specific data point was included in the training set, directly addressing the core risk of membership inference.
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.