balance speed and accuracy. It does not use traditional keyword indexing or automatic dimensionality reduction. What happens if an IVF index has too many partitions relative to the dataset size?
Explanation: Choosing the right similarity metric (e.g., cosine similarity or Euclidean distance) directly impacts the efficiency and accuracy of vector searches. Different similarity metrics are suited to different types of data, influencing search speed and retrieval relevance. Traditional indexing methods and foreign key constraints do not apply to high -dimensional vector searches. Which type of model is best suited for generating external vector embeddings for text data before inserting into Oracle AI Vector Search?
What is the benefit of using a multi -vector approach for multi -document search?
D. A warning is logged, but the query executes Explanation: In Oracle Database 23ai, vector indexes (e.g., HNSW, IVF) are built with a specific distance metric (e.g., cosine, Euclidean) that defines how similarity is computed. If a query specifies a different metric (e.g., querying with Euclidean on a cosine -based index), the index cannot be used effectively, and the query fails (A) with an error, as the mismatch invalidates the index’s structure. An exact match search (B) doesn’t occur automatically; Oracle requires explicit control. The index doesn’t update itself (C), and warnings (D) are not the default behavior —errors are raised instead. Oracle’s documentation mandates metric consistency for index usage. Reference: Oracle Database 23ai AI Vector Search Guide, Section on Vector Index Metrics. What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle AI Vector Search?
encoding is not suitable for vector storage. Which capability of Select AI with Autonomous simplifies AI -powered data retrieval?
What is the primary limitation of using Euclidean distance for similarity search?
