A. Native Support for Vector Search Only within the Database Server
B. Vector Replication with GoldenGate
C. AI Smart Scan
D. Loading Vector Data using SQL*Loader
A. UPDATE
B. SELECT
C. JOIN ON VECTOR columns
D. DELETE
A. It compares every vector in the dataset
B. It trades off accuracy for faster performance
C. It always guarantees 100% accuracy
D. It is slower than exact similarity search
A. A warning is logged, but the query executes
B. The index automatically updates
C. The query fails
D. An exact match search is triggered
A. To execute similarity search operations within a database
B. To define the schema for a vector database
C. To transform text or data into numerical vector representations
D. To store vectors in a structured format for efficient retrieval
A. To generate a single vector embedding for data
B. To serialize vectors into a string
C. To calculate vector distances
D. To calculate vector dimensions
A. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing
B. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
C. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy
D. Both operate identically but differ in memory usage
