1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A company is deploying a RAG system for internal document search using OCI OpenSearch as the vector store. Users report that queries about recent policy changes return no results, even though the new policies were ingested. Which configuration is most likely missing?
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 vector search index must have a refresh interval set to immediate.
OCI OpenSearch by default has a refresh interval (e.g., 1 second), which means recently ingested documents may not appear in search results immediately. Setting the refresh interval to 'immediate' forces the index to refresh after every write, making new documents searchable right away. Option A (hybrid search) improves relevance but does not affect document availability. Option B (normalizing embeddings) is important for cosine similarity but not for search availability. Option D (text-splitting chunker) is a preprocessing step for ingestion but does not control when documents become searchable.
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 query should use a hybrid search combining keyword and vector.
Why it's wrong here
Hybrid search improves relevance but does not affect document visibility.
- ✗
The embeddings must be normalized before indexing.
Why it's wrong here
Normalization is a best practice but not required for search availability.
- ✓
The vector search index must have a refresh interval set to immediate.
Why this is correct
Without immediate refresh, new documents may not be visible in search results.
- ✗
The ingestion pipeline should use a text-splitting chunker.
Why it's wrong here
Chunking affects quality but is not the cause of missing results.
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