1Z0-1127-25 LLM Fundamentals Practice Question
An LLM generates a response that contains a plausible-sounding but factually incorrect statement about a historical event. This is an example of which known limitation?
⚠ Common exam trap
A common pitfall is confusing hallucination with knowledge cutoff. Hallucination generates incorrect information even on topics within the training data's timeframe, while knowledge cutoff only explains missing recent events. This question tests the ability to distinguish these two limitations.
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 content that is plausible-sounding but factually incorrect or nonsensical. This occurs when the model's probabilistic next-token prediction produces statements that are not grounded in its training data or real-world facts, often due to overgeneralization or lack of factual recall mechanisms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Knowledge cutoff
Why it's wrong here
Knowledge cutoff means the model lacks information after a certain date, not that it invents false facts.
- ✓
Hallucination
Why this is correct
Hallucination is when the model produces factually incorrect or nonsensical content that appears plausible.
- ✗
Bias in training data
Why it's wrong here
Bias leads to skewed or stereotyped outputs, not necessarily factual errors about specific events.
- ✗
Context length constraint
Why it's wrong here
Context length constraint limits how much input the model can process, not the accuracy of generated content.
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