1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
Which TWO are best practices for building a RAG application on OCI? (Choose two.)
⚠ Common exam trap
Oracle often tests the misconception that real-time embedding generation or full-text search can substitute for precomputed vector indexes in RAG, when in practice latency and semantic alignment requirements make pre-indexing and ANN search mandatory.
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
✓
Use a vector database such as OCI OpenSearch with ANN indexes for storing embeddings.
OCI OpenSearch with Approximate Nearest Neighbor (ANN) indexes is a best practice for vector storage and retrieval in RAG applications. ANN indexes enable efficient similarity search over high-dimensional embeddings, which is essential for retrieving relevant context from large document collections at low latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a vector database such as OCI OpenSearch with ANN indexes for storing embeddings.
Why this is correct
ANN indexes enable fast similarity search.
- ✗
Generate embeddings for documents at query time to ensure freshness.
Why it's wrong here
Generating embeddings at query time adds latency; pre-computing is preferred.
- ✓
Pre-index the documents and update the index periodically to reflect new content.
Why this is correct
Periodic indexing ensures the RAG system uses up-to-date information.
- ✗
Store the source documents only in OCI Object Storage and retrieve them at query time using full-text search.
Why it's wrong here
Full-text search is slower and less semantically aware than vector search.
- ✗
Use a different embedding model for documents and queries to capture distinct semantics.
Why it's wrong here
Using the same model ensures embeddings are in the same vector space.
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