AI0-001 AI Security, Ethics and Governance Practice Question
A company implements an AI-based chatbot for customer service. After deployment, customers report that the chatbot sometimes uses offensive language. The development team reviews the training data and finds no explicit offensive content. What is the most likely explanation?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between explicit data contamination (poisoning) and implicit bias learned from benign-looking data, so the trap here is assuming that the absence of explicit offensive content in the training data means the model cannot produce offensive output.
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
✓
The model learned biased language patterns from the training corpus
The chatbot's offensive language likely stems from biased or toxic patterns present in the training corpus, even if no explicit offensive content was flagged. Large language models learn statistical associations from their training data, and if the corpus contains subtle biases, stereotypes, or indirect toxic language, the model can reproduce these patterns in its responses. This is a well-known issue in AI ethics and governance, where models inadvertently amplify societal biases embedded in the data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
There is a bug in the deployment pipeline
Why it's wrong here
A bug could cause errors but not specifically offensive language; it's more likely a model behavior issue.
- ✗
The model is overfitting to rare examples
Why it's wrong here
Overfitting can cause erratic outputs, but offensive language typically stems from biased patterns.
- ✓
The model learned biased language patterns from the training corpus
Why this is correct
The model may have learned offensive language from context, e.g., associating certain demographics with negative terms.
- ✗
The training data was poisoned by an attacker
Why it's wrong here
Poisoning would introduce malicious examples, but the team found no offensive content in training data.
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.