1Z0-1127-25 LLM Fundamentals Practice Question
An LLM is being used to answer customer queries about a product catalog. The answers are fluent but sometimes include plausible-sounding but incorrect product details. What is this phenomenon called, and which technique is most effective to mitigate it?
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; use Retrieval-Augmented Generation (RAG) with the catalog indexed
Hallucination is the generation of false information; RAG grounds responses in retrieved factual documents, reducing hallucinations.
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; fine-tune the model on the catalog
Why it's wrong here
Knowledge cutoff refers to training data date, but fine-tuning may not eliminate hallucinations.
- ✓
Hallucination; use Retrieval-Augmented Generation (RAG) with the catalog indexed
Why this is correct
Hallucination is the correct term; RAG is the standard mitigation.
- ✗
Bias amplification; increase temperature
Why it's wrong here
Bias is a different issue; increasing temperature worsens hallucinations.
- ✗
Overfitting; reduce the model size
Why it's wrong here
Overfitting is not the primary issue here.
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