Building LLM Applications with RAG and Vector Search →mediumMultiple ChoiceObjective-mapped

1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Which OCI service provides a managed vector database capability that can be used as a knowledge base in a RAG architecture?

⚠ Common exam trap

Oracle often tests the misconception that any database with vector support (like Autonomous Database) is the primary managed vector service, but the question specifically asks for a 'managed vector database capability' as a knowledge base in RAG, and OCI Search with OpenSearch is the dedicated service designed for this purpose.

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 provides a managed vector database capability through its k-nearest neighbor (k-NN) plugin, which supports storing and querying vector embeddings. This makes it suitable as a knowledge base in a Retrieval-Augmented Generation (RAG) architecture, where vector similarity search retrieves relevant context for LLM prompts.

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 MySQL HeatWave
Why it's wrong here
HeatWave does not provide vector search capabilities.
✗
OCI Database (Autonomous Database)
Why it's wrong here
Autonomous Database does not have native vector search (though it may via extensions, it's not managed vector database).
✓
OCI Search with OpenSearch
Why this is correct
OpenSearch includes the k-NN plugin for vector search, managed by OCI.
✗
OCI Object Storage
Why it's wrong here
Object Storage is for storing blobs, not for vector indexing.

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1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Why each option matters

Option-by-option breakdown

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