1Z0-1127-25 LLM Fundamentals Practice Question
A team is implementing a RAG pipeline in OCI. They have a large collection of PDF documents. After chunking and embedding the documents, retrieval quality is poor. Which step is MOST likely the root cause?
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
✓
The chunk size is too large, causing each chunk to contain multiple topics
Chunking strategy (size and overlap) directly affects how well the retrieval step can find relevant passages. Too large or poorly split chunks can dilute semantic meaning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The retrieval step uses greedy decoding
Why it's wrong here
Greedy decoding is a text generation parameter, not relevant to retrieval.
- ✓
The chunk size is too large, causing each chunk to contain multiple topics
Why this is correct
Large chunks dilute the semantic focus, making it hard for the retriever to find passages relevant to a specific query.
- ✗
The embedding model is a generation model, not an embedding model
Why it's wrong here
Using a generation model would not produce meaningful embeddings, but the question states they embedded the documents, implying they used an embedding model.
- ✗
Cosine similarity is not appropriate for comparing embeddings
Why it's wrong here
Cosine similarity is standard for comparing dense embeddings.
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