1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A developer is troubleshooting low recall in a vector search. Which THREE factors should be checked? (Choose three.)
⚠ Common exam trap
Oracle often tests the misconception that retrieval parameters like k or generation parameters like temperature affect recall, when in fact recall is primarily determined by embedding quality, chunking strategy, and query embedding fidelity.
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
✓
Embedding model quality and relevance to domain
The embedding model's quality and domain relevance directly determine how well semantic relationships are captured. If the model is not fine-tuned on domain-specific data, it may fail to map similar concepts close together in the vector space, leading to low recall. For example, a general-purpose model may not distinguish between 'bank' as a financial institution versus a river bank in a legal document search.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Embedding model quality and relevance to domain
Why this is correct
A model not trained on similar data may produce poor embeddings.
- ✓
Chunk size and overlap strategy
Why this is correct
Improper chunking can cause important information to be missed or poorly represented.
- ✓
Quality of the query embedding generation
Why this is correct
If the query is not embedded correctly, the search will not align with semantic intent.
- ✗
The number of results returned (k) in the search
Why it's wrong here
While k affects recall, the question is about underlying factors causing low recall, not tuning parameters.
- ✗
The LLM's temperature setting
Why it's wrong here
Temperature affects generation randomness, not retrieval recall.
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