Generative AI Leader Fundamentals of Generative AI Practice Question
A media company is using a generative AI model to create short video scripts. They notice that the model sometimes produces content that is factually incorrect or nonsensical. Which term best describes this phenomenon?
⚠ Common exam trap
The trap here is conflating hallucination with bias or overfitting, which are distinct issues with different causes and mitigations.
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 is the term for generative AI producing incorrect or nonsensical information that appears confident. In this scenario, the model's factually incorrect script content is a direct example. Other options like overfitting, bias, or underfitting describe different issues and do not capture the specific problem of generating false but plausible text.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Hallucination
Why this is correct
Hallucination refers to a generative AI model producing confident but incorrect or nonsensical information. In this scenario, the model generates factually incorrect script content, which is a classic example of hallucination. This occurs because the model predicts likely sequences without grounding in verified facts, leading to plausible-sounding errors.
- ✗
Bias
Why it's wrong here
Bias refers to systematic and unfair discrimination in model outputs, often reflecting societal prejudices. While bias is a concern, the scenario describes factual inaccuracies, not prejudiced or unfair treatment. Bias might manifest as stereotypes, but it does not specifically cause nonsensical or false statements, so it is not the best term here.
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Underfitting
Why it's wrong here
Underfitting occurs when a model is too simple to capture underlying patterns, leading to poor performance on both training and new data. The scenario describes a model that generates plausible but incorrect content, which indicates it has learned patterns but lacks factual grounding. Underfitting would result in obviously poor outputs, not confident falsehoods.
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Overfitting
Why it's wrong here
Overfitting occurs when a model performs well on training data but poorly on new data. While it can cause poor generalization, the scenario describes plausible but incorrect outputs, not a failure to generalize. Overfitting is typically addressed with regularization or more data, but it does not directly explain the generation of nonsensical facts.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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