1Z0-1127-25 LLM Fundamentals Practice Question
A company's AI system uses RAG to answer customer questions. Users often get incomplete answers because the retrieved chunks do not contain all relevant information. Which step in the RAG pipeline is most likely the issue?
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 strategy (chunk size and overlap)
Chunking determines how documents are split into pieces. If chunks are too small, key information may be split across chunks, causing incomplete retrieval. Adjusting chunk size and overlap can improve completeness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrieval top-k setting
Why it's wrong here
Increasing top-k might retrieve more chunks, but if each chunk is incomplete, the answer may still be incomplete.
- ✗
Generation model temperature
Why it's wrong here
Temperature affects creativity, not the completeness of retrieved information.
- ✓
Chunking strategy (chunk size and overlap)
Why this is correct
If chunks are too small or have insufficient overlap, relevant information may be split, leading to incomplete retrieval.
- ✗
Embedding model selection
Why it's wrong here
While embedding model matters, the primary issue here is likely that chunks are not capturing complete information.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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