B. It applies rule -based filtering before executing a vector similarity query. C. It converts vectors into relational database rows for faster indexing. D. It uses Approximate Nearest Neighbor (ANN) algorithms to reduce search time. Explanation: Oracle AI Vector Search utilizes Approximate Nearest Neighbor (ANN) algorithms to speed up high - dimensional vector searches. ANN techniques, such as Hierarchical Navigable Small World (HNSW) graphs or Locality -Sensitive Hashing (LSH), allow for efficient retrieval of similar vectors without having to compare every single vector in the dataset. This significantly improves search performance in large -scale applications. Unlike relational data structures or text-based storage, vector databases require specialized indexing methods to optimize similarity queries. How should a RAG application pre -process text before embedding generation?
clusters, perform exhaustive comparisons, or modify execution pathways for predefined distance scores. Which SQL Loader feature optimizes bulk loading of vector embeddings?
Exact similarity search is slow because every query is compared against all stored vectors, making retrieval time scale poorly. It does not require partition updates, probabilistic models, or precomputed lookup tables. When integrating a RAG pipeline with Oracle AI Vector Search, what is the key role of vector embeddings?
C. Strings of comma -separated values for easy database insertion D. Encoded categorical variables representing vectorized data Explanation: Oracle AI Vector Search expects arrays of floating -point numbers for similarity calculations. Using strings or nested dictionaries would require extra processing, increasing latency. Categorical variables do not effectively represent continuous vector space relationships. What role does cosine similarity play in Oracle AI Vector Search?
Which factor most significantly affects the accuracy of an approximate similarity search?
➢ TOTAL QUESTIONS: 360 Which Oracle feature enhances performance when generating vector embeddings at scale?
