1Z0-184-25試験無料問題集「Oracle AI Vector Search Professional 認定」

A. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS

B. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS

C. DBMS_VECTOR_CHAIN.UTL_TO_TEXT

D. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT

A. To fetch rows that match exact vector embeddings

B. To calculate the distance between vectors using a specified metric

C. To group vectors by their exact scores

D. To create vector indexes for efficient searches

A. To reduce the computational burden on the embedding model

B. To minimize token truncation as each vector embedding model has its own maximum token limit

C. To facilitate parallel processing of the data during vectorization

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

A. Creating a new table using CTAS (CREATE TABLE AS SELECT) that includes the VECTOR column from the original table

B. Dropping an existing VECTOR column from the table

C. Modifying the data type of an existing VECTOR column to a non-VECTOR type

D. Adding a new VECTOR column to the table