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1Z0-1127-25 LLM Fundamentals Practice Question

Which of the following is a key limitation of large language models that RAG (Retrieval-Augmented Generation) aims to address?

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

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Hallucinations (factual errors)

RAG addresses hallucinations by grounding the model's output in retrieved documents that contain factual information. Knowledge cutoff, bias, and context length constraints are separate issues that RAG may partially help with, but its primary purpose is to reduce factual errors.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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Hallucinations (factual errors)
Why this is correct
RAG retrieves factual documents from a knowledge base and provides them as context, significantly reducing the likelihood of the model generating incorrect facts.
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Context length constraints
Why it's wrong here
RAG can help by retrieving only relevant chunks instead of fitting all information into the context, but that is a secondary benefit.
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Bias in training data
Why it's wrong here
RAG does not directly address bias; it may even amplify biases present in the retrieved documents.
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Knowledge cutoff date
Why it's wrong here
RAG can provide recent information, but its main goal is to reduce hallucinations by providing relevant context.

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1Z0-1127-25 LLM Fundamentals Practice Question

Why each option matters

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