1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A developer is building a RAG pipeline using OCI Data Science and wants to store vector embeddings. Which OCI service is optimized for vector search and can be used as a vector store?
⚠ Common exam trap
Test-takers frequently confuse OCI Autonomous Database's ability to store vectors with being optimized for vector search, overlooking that OpenSearch is purpose-built for high-performance vector similarity search with native k-NN support.
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
✓
OCI OpenSearch
B is correct because OCI OpenSearch is a fully managed, search and analytics engine that natively supports k-nearest neighbor (k-NN) search on dense vector embeddings. It provides optimized indexing and querying for high-dimensional vectors, making it the ideal vector store for a RAG pipeline in OCI Data Science.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
OCI Autonomous Database
Why it's wrong here
While Autonomous Database has AI Vector Search, OpenSearch is more commonly used for dedicated vector stores in RAG pipelines.
- ✓
OCI OpenSearch
Why this is correct
OCI OpenSearch includes a vector database plugin for k-NN similarity search, making it a suitable vector store.
- ✗
OCI Object Storage
Why it's wrong here
Object Storage is for storing files, not for similarity search on vectors.
- ✗
OCI Streaming
Why it's wrong here
Streaming is for real-time data ingestion, not for storing or querying vectors.
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