AI0-001 AI Concepts and Foundations Practice Question
A company deploys a chatbot using a large language model (LLM). After launch, users report that the chatbot sometimes generates plausible but false information. This phenomenon is known as:
⚠ Common exam trap
Watch out — candidates often confuse hallucination with overfitting, thinking the model is 'making up' data due to memorization errors, but overfitting is about poor generalization to new inputs, not confident false outputs from a well-generalized model.
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
✓
Hallucination
Hallucination in LLMs refers to the generation of plausible but factually incorrect or nonsensical information. This occurs when the model's probabilistic next-token prediction produces confident-sounding outputs that deviate from training data or real-world facts, often due to insufficient grounding or training data gaps.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Gradient explosion
Why it's wrong here
Gradient explosion causes unstable training with diverging loss and NaN weights, not false statements at inference. It is tempting because it is a well-known failure mode in deep networks, and it is the correct answer when training collapses rather than converging.
- ✗
Overfitting
Why it's wrong here
Overfitting describes a model memorising training data and performing poorly on unseen inputs, not fabricating facts. It is tempting because both surface as wrong outputs, and overfitting is the correct diagnosis when validation loss rises while training accuracy stays high.
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Concept drift
Why it's wrong here
Concept drift is the decay of model accuracy as real-world data distributions change over time, requiring retraining. It is tempting because chatbot errors appear after launch, but drift produces degrading predictions on shifted inputs, not fluent fabrications on unchanged ones.
- ✓
Hallucination
Why this is correct
Hallucination describes an LLM producing fluent, confident output that is factually wrong because the model predicts plausible token sequences rather than retrieving verified facts. The stem's "plausible but false" wording maps directly onto this term, not bias, overfitting or latency.
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.