1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
An organization stores its knowledge base in Oracle Autonomous Database and wants to build a RAG chatbot using OCI Generative AI. The chatbot must retrieve the most relevant documents based on user queries. Which indexing approach is BEST suited for efficient similarity search on text embeddings?
⚠ Common exam trap
Oracle often tests the misconception that any index type can be applied to vector columns, but the trap here is that candidates confuse traditional database indexes (B-tree, bitmap, inverted) with specialized vector indexes, failing to recognize that only ANN indexes support distance-based similarity search on embeddings.
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
✓
Create an ANN index on the embedding vector column.
Approximate Nearest Neighbor (ANN) indexes are specifically designed for high-dimensional vector spaces, enabling efficient similarity search on embedding vectors. In Oracle Autonomous Database, ANN indexes (e.g., using IVF or HNSW algorithms) drastically reduce search latency compared to brute-force scans, which is critical for real-time RAG chatbot responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create an ANN index on the embedding vector column.
Why this is correct
ANN indexes enable fast approximate nearest neighbor search in vector databases.
- ✗
Create a bitmap index on the embedding vector column.
Why it's wrong here
Bitmap indexes are not suitable for continuous vector data.
- ✗
Create an inverted index on the document text column.
Why it's wrong here
Inverted indexes support keyword search, not semantic similarity.
- ✗
Create a B-tree index on the document text column.
Why it's wrong here
B-tree indexes are inefficient for vector similarity search.
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