Dimensional consistency ensures embeddings are properly aligned for similarity calculations. Unlike primary keys or relational schema design, which are relevant for structured data, vector indexing relies on dimensional alignment rather than schema constraints. Which step is required before inserting externally generated embeddings into Oracle AI Vector Search?
Explanation: The DBMS_AI_SEARCH.SEARCH_SIMILARITY function is specifically designed for retrieving similar vector embeddings in Oracle AI Vector Search. The other options are not valid Oracle SQL functions for executing vector similarity searches. Which Oracle AI Vector Search technique improves multi -vector query efficiency?
C. Assigning a fixed -length identifier to each stored embedding D. Storing vectors in separate database tables for faster retrieval Explanation: When creating an IVF index, specifying the number of partitions is crucial because it determines how vectors are grouped. Identifiers are not mandatory, precomputing similarity scores is not part of the standard process, and storing vectors in separate tables does not directly improve performance. Which factor most affects the computational cost of inserting a new vector into an HNSW index?
Why is Euclidean distance commonly used in vector similarity searches?
Which approach enhances response consistency when designing a RAG application?
Storing embeddings as a vector data type allows databases to perform efficient similarity searches using optimized indexing structures like HNSW. This significantly improves retrieval times for AI -powered applications. Unlike structured databases that rely on SQL joins and constraints, vector searches focus on semantic similarity rather than exact matches. Which Oracle feature helps optimize vector storage when handling large -scale embeddings?
