AI0-001 AI Security Practice Question
A company is deploying an AI model that processes financial transactions. They want to implement privacy-preserving machine learning. Which THREE techniques achieve this goal? (Select three.)
⚠ Common exam trap
CompTIA often tests the distinction between techniques that improve model performance (pruning, augmentation) versus those that actively protect data privacy (differential privacy, encryption, federated learning), so candidates mistakenly select performance-enhancing options as privacy-preserving ones.
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 (B) is correct because it adds calibrated noise (e.g., via the Laplace or Gaussian mechanism) to computations or gradients so that any single individual's transaction data has a bounded influence on the model output, providing a formal privacy guarantee. Homomorphic encryption (D) is correct because it allows computations to be performed directly on encrypted financial data (e.g., using schemes like Paillier, BFV, or CKKS), so the model can train or infer without ever decrypting sensitive values. Federated learning (E) is correct because it keeps raw transaction data on local devices or silos and only shares model updates (often combined with secure aggregation or differential privacy), minimizing centralized exposure of private records. Model pruning (A) merely removes redundant weights to reduce model size and compute, and data augmentation (C) synthetically expands training data for robustness; neither provides a privacy guarantee, so they do not belong.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model pruning
Why it's wrong here
Model pruning removes redundant weights to shrink the network and speed inference; it operates on learned parameters, leaving training data and outputs untouched, so no privacy guarantee is added. Pruning suits deployment efficiency on constrained hardware, not privacy-preserving machine learning.
- ✓
Differential privacy
Why this is correct
Differential privacy adds calibrated noise to computations or training data so that any single individual's contribution cannot be inferred from outputs. This provides a mathematical privacy guarantee for the financial transaction data, directly achieving the privacy-preserving machine learning objective.
- ✗
Data augmentation
Why it's wrong here
Data augmentation generates additional synthetic training samples to improve generalisation; it neither encrypts, perturbs nor anonymises the underlying transaction data, so raw records remain exposed. It belongs in model accuracy and class-imbalance work, not privacy preservation.
- ✓
Homomorphic encryption
Why this is correct
Homomorphic encryption allows computation directly on encrypted data, so the model processes transactions without ever decrypting them. This satisfies the privacy-preserving requirement: plaintext financial data never becomes visible to the model or infrastructure, protecting confidentiality throughout training and inference.
- ✓
Federated learning
Why this is correct
Federated learning trains a shared model across distributed devices while raw transaction data remains local, exchanging only model updates. This satisfies the privacy-preserving constraint by preventing sensitive financial records from centralising, so no single party ever accesses the underlying personal data.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.