Explanation: The VECTOR_EMBEDDING function in Oracle 23ai (D) generates a vector embedding from input data (e.g., text) using a specified model (e.g., ONNX), producing a single VECTOR -type output for similarity search or AI tasks. It doesn’t calculate dimensions (A); VECTOR_DIMENSION_COUNT does that. It doesn’t compute distances (B); VECTOR_DISTANCE is for that. It doesn’t serialize vectors (C); VECTOR_SERIALIZE handles serialization. Oracle’s documentation positions VECTOR_EMBEDDING as the core function for in -database embedding creation, central to vector search workflows. Reference: Oracle Database 23ai SQL Language Reference, Section on VECTOR_EMBEDDING. What is a key characteristic of HNSW vector indexes?
AI Smart Scan in Exadata System Software 24ai (B) accelerates AI workloads, including vector search, by ofloading processing to storage servers using Exadata’s RDMA Memory (XRMEM), Smart Cache, and on -storage capabilities. This enhances performance for large -scale vector operations. Real -time monitoring (A) isn’t its focus; that’s for management tools. Queryoptimization (C) is a general Exadata feature (Smart Scan), but AI Smart Scan specifically targets AI tasks. Oracle’s 24ai documentation emphasizes its role in speeding up AI computations. Reference: Oracle Exadata System Software 24ai Release Notes, Section on AI Smart Scan. Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?
exact search (C) adds overhead without speed gains. Exact search with high accuracy (D) maximizes precision but sacrifices speed. Oracle’s documentation recommends ANN for large -scale, speed -focused queries. Reference: Oracle Database 23ai AI Vector Search Guide, Section on Approximate Search. Which is a characteristic of an approximate similarity search in Oracle Database 23ai?
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?
lead to conflating data preparation with query execution, a common pitfall for beginners. Reference: Oracle Database 23ai AI Vector Search Guide, Chapter on Vector Embeddings; SQL Language Reference, VECTOR_EMBEDDING. What is the significance of using local ONNX models for embedding within the database?
➢ TOTAL QUESTIONS: 360 Which Oracle feature enhances performance when generating vector embeddings at scale?
