NCA-GENL Core Machine Learning and AI Knowledge Practice Question
A machine learning engineer is evaluating a large language model (LLM) on a text generation task. They observe that the model produces coherent and fluent sentences, but the content is factually incorrect and sometimes contradicts known facts. Which term best describes this phenomenon?
⚠ Common exam trap
Many candidates confuse hallucination with overfitting or mode collapse, but hallucination specifically refers to plausible yet false content, not training issues or lack of diversity.
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
The phenomenon described—fluent but factually incorrect text generation—is known as hallucination. It occurs because LLMs are trained to predict likely sequences of words, not to verify facts. Hallucination is a significant challenge for deploying LLMs in applications requiring factual accuracy, and it can be mitigated with retrieval-augmented generation or factual consistency checks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Catastrophic forgetting
Why it's wrong here
Catastrophic forgetting is when a neural network forgets previously learned information upon learning new tasks. This scenario does not involve sequential task learning or forgetting; it concerns the generation of incorrect facts. Thus, catastrophic forgetting is not the correct term for this phenomenon.
- ✓
Hallucination
Why this is correct
Hallucination refers to the generation of plausible-sounding but factually incorrect or nonsensical content by an LLM. In this scenario, the model produces fluent text that contradicts known facts, which is the hallmark of hallucination. It is a known challenge in generative AI, especially when models are not grounded in external knowledge.
- ✗
Overfitting
Why it's wrong here
Overfitting occurs when a model performs well on training data but poorly on unseen data. Here, the model generates fluent but factually wrong text, which is not necessarily due to overfitting; it could be underfitting or simply lacking factual grounding. Overfitting would typically manifest as poor generalization to new inputs, not as confident false statements.
- ✗
Mode collapse
Why it's wrong here
Mode collapse is a failure mode in generative adversarial networks where the generator produces limited varieties of outputs, often repeating the same sample. This scenario describes factual errors in text, not a lack of diversity in generated samples. Mode collapse is not applicable to LLM text generation in this context.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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