candidate vectors, though at the cost of increased computational complexity. It does not reduce memory consumption, decrease distance calculations, or require extra metadata storage. What is the primary characteristic of an exact similarity search in vector databases?
Explanation: Hierarchical Navigable Small World (HNSW) indexing with workload -aware tuning optimizes vector search performance on Exadata AI Storage. Locality -sensitive hashing, partitioning, and batch normalization do not offer the same level of optimization for AI -driven vector retrieval. Which primary risk exists when deleting vector embeddings from a table?
Which function is used to generate vector embeddings within an Oracle database?
C. It optimizes search efficiency by limiting the number of candidates D. It enhances scalability by dynamically reorganizing stored vectors Explanation: Exact similarity search ensures that the retrieved neighbors are the most accurate, unlike approximate methods, which prioritize efficiency by limiting candidates. It does not enhance scalability through reorganization or rely on partitioning. Which distance function is least suitable for comparing high -dimensional vectors?
How does multi -vector similarity search enhance document retrieval?
Traditional indexing, rule-based structures, and categorized filters are useful but do not provide AI-driven query optimization at the same level. Which factor must be considered when generating vector embeddings inside Oracle?
