1Z0-1127-25 Using OCI Generative AI Service Practice Question
A team is building a Retrieval-Augmented Generation (RAG) pipeline using OCI Generative AI. They need to store and retrieve document embeddings for semantic search. Which OCI service is most appropriate as the vector store?
⚠ Common exam trap
Candidates often assume that OCI Autonomous Database with AI Vector Search is the best choice since it supports vectors, but for a dedicated vector store in a RAG pipeline, OCI Search with OpenSearch provides a specialized vector search engine with native k-NN support and direct integration with OCI Generative AI, making it the most appropriate option.
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 Search with OpenSearch
OCI Search with OpenSearch is the most appropriate vector store for a RAG pipeline because it natively supports storing and querying high-dimensional vector embeddings using the k-nearest neighbor (k-NN) algorithm. It integrates directly with OCI Generative AI to enable semantic search over ingested documents, providing the required similarity search capabilities for retrieval-augmented generation.
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 Search with OpenSearch
Why this is correct
OpenSearch supports vector storage and k-NN search, making it ideal for RAG pipelines.
- ✗
OCI Streaming
Why it's wrong here
Streaming is for real-time data ingestion, not for storing and querying vectors.
- ✗
OCI Object Storage
Why it's wrong here
Object Storage is for unstructured files, not for vector indexing and similarity search.
- ✗
OCI Autonomous Database with AI Vector Search
Why it's wrong here
Autonomous Database can store vectors but is not as optimized for high-dimensional vector similarity search as OpenSearch.
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