1Z0-184-25 Question 122
Single answerYou are designing a retrieval-augmented generation (RAG) application on Oracle Cloud Infrastructure. Your application ingests thousands of documents daily, generates embeddings using an Oracle Data Science model, and requires low-latency vector similarity searches on these embeddings. Which approach provides an optimal solution for performing real-time vector searches on OCI?
- A
Store embeddings as JSON files in OCI Object Storage and scan them each time a query is made.
- B
Use Oracle NoSQL Database without any specialized indexing to store and retrieve embeddings.
- C
Utilize MySQL HeatWave with vector store functionality for storing and querying embeddings.
- D
Insert embeddings into an Autonomous Database table without enabling vector-based search features.
Show answer and explanation
Correct answer: C
Explanation
Retrieval-augmented generation (RAG) applications require effective vector indexing and search capabilities to retrieve relevant context from large document sets. Although Object Storage and NoSQL can store embedding data, they do not inherently offer optimized vector-based queries for real-time inference. MySQL HeatWave with vector store functionality allows you to index and query embeddings efficiently, making it a practical choice for RAG use cases in OCI. Refer to Oracle documentation on MySQL HeatWave vector indexing best practices for more information on optimizing performance and scalability.
- A. Incorrect.
Although OCI Object Storage is cost-effective for large files, it does not provide built-in vector search indexing. Scanning thousands of JSON files on every query would be inefficient and slow.
- B. Incorrect.
Oracle NoSQL Database can store large datasets, but if you do not enable specialized indexing (such as secondary indexes on vectors), it won�t efficiently support real-time vector similarity searches.
- C. Correct.
MySQL HeatWave with vector store functionality efficiently handles vector-based queries. This solution enables low-latency vector similarity searches at scale, which suits RAG applications.
- D. Incorrect.
While Autonomous Database can handle complex queries, simply inserting embeddings without enabling native vector-based search or indexing would not achieve the required low-latency semantic lookups.