- A
Redesign the training pipeline to incorporate a reputation system for reporting users
A reputation system filters out malicious reports, preventing poisoning at the source.
- B
Increase the weight of non-reported posts to counteract the reported posts' influence
Why wrong: Weighting may not fully correct the poisoned patterns.
- C
Apply adversarial training to make the model robust to crafted inputs
Why wrong: Adversarial training targets input perturbations, not training data poisoning.
- D
Retrain the model on a dataset that excludes all user-reported posts
Why wrong: Excluding all reported posts loses valuable training data and may not eliminate poisoning.
Question 771 of 1,000
AI0-001 AI Security, Ethics and Governance Practice Question
This AI0-001 practice question tests your understanding of ai security, ethics and governance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 filters out malicious reports, preventing poisoning at the source.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the weight of non-reported posts to counteract the reported posts' influence
Why it's wrong here
Weighting may not fully correct the poisoned patterns.
- ✗
Apply adversarial training to make the model robust to crafted inputs
Why it's wrong here
Adversarial training targets input perturbations, not training data poisoning.
- ✗
Retrain the model on a dataset that excludes all user-reported posts
Why it's wrong here
Excluding all reported posts loses valuable training data and may not eliminate poisoning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
A reputation system in the training pipeline typically assigns a trust score to each reporting user based on historical accuracy of their reports, using a Bayesian or weighted averaging approach. The model then applies a per-sample loss weight inversely proportional to the reporter's reputation, effectively down-weighting or ignoring reports from low-reputation users. In practice, this is implemented as a preprocessing step that filters or reweights training examples before they enter the gradient update loop, preventing the poisoned data from biasing the model's parameters.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Redesign the training pipeline to incorporate a reputation system for reporting users — Option A is correct because 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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