1Z0-1127-25 LLM Fundamentals Practice Question
A developer is debugging a RAG pipeline where the LLM frequently ignores retrieved documents and produces hallucinations. Which THREE factors could contribute to this problem?
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 prompt does not explicitly instruct the model to base its answer on the provided context
Low chunk overlap can cause loss of context, improper prompt instructions may lead the model to ignore retrieved content, and a small top-K retrieval may miss relevant documents. A large embedding dimension improves retrieval precision, and a high temperature increases randomness but does not cause ignoring documents.
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 generation temperature is set to 0
Why it's wrong here
Temperature 0 makes generation deterministic and greedy, which does not cause the model to ignore documents.
- ✓
The prompt does not explicitly instruct the model to base its answer on the provided context
Why this is correct
If the prompt does not instruct the model to use the retrieved documents, it may default to its internal knowledge.
- ✓
The retrieval top-K parameter is set too low
Why this is correct
If too few chunks are retrieved, the model may not have enough relevant information, leading to hallucinations.
- ✗
The embedding model's output dimension is too large
Why it's wrong here
A larger embedding dimension generally improves retrieval fidelity, so it would not cause hallucinations.
- ✓
The chunking strategy uses zero overlap between consecutive chunks
Why this is correct
Without overlap, sentences or concepts may be split across chunks, losing context and causing the model to rely on its own knowledge.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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