1Z0-1127-25 LLM Fundamentals Practice Question

Which TWO components are essential in a Retrieval-Augmented Generation (RAG) pipeline?

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

✓

Chunking the documents into smaller pieces

Chunking splits documents, embedding converts chunks to vectors, retrieval fetches relevant chunks, and generation produces the answer.

Answer analysis

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

✓
Chunking the documents into smaller pieces
Why this is correct
Chunking is necessary for indexing.
✓
Embedding the chunks into a vector space
Why this is correct
Embedding enables similarity search.
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Fine-tuning the LLM on the documents
Why it's wrong here
Fine-tuning is not part of RAG; RAG uses retrieval without retraining.
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Knowledge distillation
Why it's wrong here
Distillation is a compression technique, not part of RAG.
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Beam search decoding
Why it's wrong here
Beam search is a decoding strategy, not essential to RAG.

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