Which indexing method should be applied when storing vector embeddings for efficient queries?
similarity lookups, and normalization is not suitable for high -dimensional embeddings. What is the primary purpose of using a vector data type for embeddings in an AI -driven database?
C. The total number of records stored in the relational database schema.. D. The use of primary key constraints to enforce data integrity. Explanation: The dimensionality of vector embeddings depends on the complexity of the relationships they represent. Higher -dimensional embeddings can capture more nuanced details, but they also increase computational cost. A balance must be struck to ensure efficient similarity searches while preserving meaningful representations of data. Which component is primarily responsible for storing and retrieving high-dimensional vector embeddings in Oracle AI Vector Search?
What makes multi -vector similarity search beneficial for large document retrieval?
introduces noise, semi -structured formats do not enhance retrieval accuracy, and heuristic -based ranking lacks contextual awareness. What is the primary drawback of using brute -force exact similarity search for large datasets?
Explanation: L2 normalization ensures that all embeddings have a unit norm, which is essential for computing cosine similarity correctly. Without normalization, vector magnitudes can distort similarity scores. While dimensionality reduction and sparse matrices can be useful in some cases, they are not always necessary. Base64 encoding is irrelevant to similarity search. Which design consideration is essential for optimizing a RAG workflow in Oracle AI Vector Search?
