AI0-001 AI Security Practice Question
A company is building an AI-based resume screening tool. They want to ensure the system is secure against data poisoning attacks during the training phase. Which THREE of the following are appropriate defensive measures?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between training-phase attacks (data poisoning) and inference-phase attacks (evasion), so candidates mistakenly apply inference-time defenses like input sanitization to training security.
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
✓
Use robust statistical methods (e.g., trimmed mean) that are less sensitive to outliers
Robust statistical methods like trimmed mean reduce the influence of outlier data points that could be injected by an adversary during training. By discarding extreme values, the model becomes less sensitive to poisoned samples, which is a key defense against data poisoning attacks that aim to corrupt the learned parameters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply input sanitization to inference-time queries
Why it's wrong here
Input sanitization at inference time defends against adversarial examples and prompt injection, not against data poisoning which occurs during training.
- ✓
Use robust statistical methods (e.g., trimmed mean) that are less sensitive to outliers
Why this is correct
Robust aggregation techniques reduce the impact of maliciously inserted outliers on the model's learned parameters.
- ✓
Validate and clean training data to remove anomalies and outliers
Why this is correct
Thorough data validation and cleaning help identify and remove poisoned samples before they influence model training.
- ✓
Restrict training data sources to trusted, verified providers only
Why this is correct
Limiting data sources reduces the attack surface for an adversary to inject poisoned data into the training pipeline.
- ✗
Implement differential privacy during model training
Why it's wrong here
Differential privacy protects against membership inference, not data poisoning; it does not prevent an attacker from corrupting training data.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 754 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.