AI0-001 AI Security, Ethics and Governance Practice Question
A social media company uses an AI content moderation system to filter hate speech. The system uses a natural language processing model trained on user reports. Recently, the model's false positive rate has increased, blocking legitimate posts. An internal audit reveals that a coordinated group of users has been falsely reporting harmless posts, causing the model to learn incorrect patterns. The company needs to address the attack and restore accuracy. The engineering team can modify the training pipeline. What is the most effective first step?
⚠ Common exam trap
CompTIA often tests the distinction between defending against data poisoning (attacks on the training data) versus evasion or adversarial examples (attacks on the model at inference), and the trap here is that candidates confuse adversarial training (Option C) as a catch-all defense, when it specifically addresses input perturbations, not poisoned labels.
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
✓
Redesign the training pipeline to incorporate a reputation system for reporting users
The root cause is a coordinated attack where malicious users exploit the reporting mechanism to poison the training data. Incorporating a reputation system into the training pipeline allows the model to weigh or filter user reports based on the trustworthiness of the reporting user, directly mitigating the impact of false reports without discarding legitimate feedback. This addresses the adversarial behavior at the source, restoring accuracy by ensuring the model learns from reliable signals.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Redesign the training pipeline to incorporate a reputation system for reporting users
Why this is correct
A reputation system weights each reporter's contribution by trustworthiness, so coordinated false reports from low-reputation accounts carry little training influence. This directly neutralises the poisoning attack described in the stem, restoring accuracy without discarding legitimate user reports.
- ✗
Increase the weight of non-reported posts to counteract the reported posts' influence
Why it's wrong here
Reweighting non-reported posts does not remove the poisoned labels; the falsely reported harmless posts still contribute incorrect gradients, just scaled differently, so the learned patterns persist. Reweighting suits class imbalance, where one legitimate label is under-represented, not label corruption introduced by coordinated false reporting, which requires removing or validating those labels.
- ✗
Apply adversarial training to make the model robust to crafted inputs
Why it's wrong here
Adversarial training hardens a model against perturbed inputs at inference, but here the corruption is in the training labels themselves, so robustness to crafted inputs does nothing to unlearn the false patterns. It would be correct against evasion attacks, not the data-poisoning attack described, which needs label validation or filtering first.
- ✗
Retrain the model on a dataset that excludes all user-reported posts
Why it's wrong here
Discarding all user-reported posts removes the poisoned labels but also the genuine hate-speech signal the model depends on, leaving no reliable positive class. It is tempting as a clean reset, yet the correct first step is filtering or auditing the malicious reports while retaining legitimate ones, since user reports remain the only labelled training source.
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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.