AI0-001 AI Security Practice Question
A financial institution uses a machine learning model to approve loans. They want to protect against membership inference attacks. Which THREE techniques are effective?
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
✓
Applying model truncation or output perturbation
Differential privacy adds noise to training, model truncation reduces overfitting (which helps prevent inference), and limiting output granularity reduces the information leaked. Shadow models are used to train attack models, not defend. Federated learning alone does not prevent inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Applying model truncation or output perturbation
Why this is correct
Reducing model complexity and perturbing outputs makes it harder to infer membership.
- ✓
Training with differential privacy
Why this is correct
Differential privacy bounds the influence of any single training point, reducing inference risk.
- ✓
Limiting the granularity of model outputs (e.g., returning scores instead of probabilities)
Why this is correct
Less detailed outputs reduce the signal available for inference.
- ✗
Implementing federated learning
Why it's wrong here
Federated learning preserves privacy of raw data but does not prevent membership inference on the final model.
- ✗
Using shadow models to distract attackers
Why it's wrong here
Shadow models are typically used by attackers, not defenders.
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.