1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
Which TWO are common causes of poor answer quality in a RAG system built on OCI Generative AI? (Choose two.)
⚠ Common exam trap
Oracle often tests the distinction between retrieval-side failures (like embedding mismatch or insufficient chunks) and generation-side parameters (like temperature or model size), so candidates mistakenly attribute poor answer quality to generation settings rather than the retrieval 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
✓
Mismatch between the embedding model's training data and the domain of the documents.
The embedding model's training data determines the semantic space in which documents and queries are represented. If the model was trained on general text (e.g., Wikipedia) but the documents are from a specialized domain (e.g., medical or legal), the embeddings will fail to capture domain-specific nuances, leading to poor retrieval relevance and thus poor answer quality in the RAG system.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Mismatch between the embedding model's training data and the domain of the documents.
Why this is correct
Domain mismatch leads to poor semantic alignment and irrelevant retrieval.
- ✗
Using a generation model that is too large for the task.
Why it's wrong here
Larger models generally perform better; size is not a common cause of poor quality.
- ✗
Setting the temperature parameter too low, causing overly deterministic outputs.
Why it's wrong here
Low temperature reduces randomness and can improve answer quality.
- ✓
Insufficient number of relevant chunks in the document corpus for the given query.
Why this is correct
If relevant content is missing, the system cannot generate accurate answers.
- ✗
Using only vector search without keyword-based fallback.
Why it's wrong here
While hybrid search can help, using only vector search is not inherently a cause of poor quality.
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