1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A developer is using OCI Data Science to create a RAG pipeline. They have ingested documents into a vector store using OCI Generative AI's text-embedding model. During testing, they notice that queries return very few results (often 0 or 1) even when the knowledge base contains relevant documents. They have set the top-k parameter to 10. What is the most likely 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 similarity threshold is set too high, filtering out most results.
A high similarity threshold (e.g., >0.9) can exclude many relevant results, leading to few or zero results even with top-k set to 10. Option B: chunk size affects the granularity of text but not directly the number of results returned; small chunks can still be retrieved if the threshold is appropriate. Option C: dimensionality of the embedding model is fixed and does not directly cause zero results; low dimensionality may reduce semantic precision but not eliminate results. Option D: distance metric affects how similarity is computed but not the count; the index can still return results ranked by the chosen metric.
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 similarity threshold is set too high, filtering out most results.
Why this is correct
A high similarity threshold filters out many results, causing very few to pass.
- ✗
The documents were chunked with too small a chunk size, losing key information.
Why it's wrong here
Chunk size may affect the quality of retrieval but not the count; even small chunks can be retrieved if they are similar enough.
- ✗
The embedding model's dimensionality is too low to capture semantic differences.
Why it's wrong here
Dimensionality is a property of the embedding model and does not directly cause zero results.
- ✗
The vector search index is not configured with the correct distance metric.
Why it's wrong here
Distance metric affects the ranking order but not the number of results returned.
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Written by Johnson Ajibi, MSc IT Security
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