최신Oracle AI Vector Search Professional - 1Z0-184-25무료샘플문제

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

B. To facilitate parallel processing of the data during vectorization

C. To reduce the computational burden on the embedding model

A. Apply relational filters and a similarity search in the query

B. Perform the similarity search without a WHERE clause

C. Use VECTOR_INDEX_HINT and NO WHERE clause

D. Use UNION ALL with vector operations

A. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS

B. DBMS_VECTOR_CHAIN.UTL_TO_TEXT

C. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT

D. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS

A. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS

B. DBMS_VECTOR_CHAIN.UTL_TO_TEXT

C. DBMS_VECTOR.CONVERT_TO_TEXT

D. DBMS_VECTOR.TEXT_TO_PLAIN

A. To store vectors in a structured format for efficient retrieval

B. To execute similarity search operations within a database

C. To transform text or data into numerical vector representations

D. To define the schema for a vector database

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

B. Adding a new VECTOR column to the table

C. Dropping an existing VECTOR column from the table

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

A. An exact match search is triggered

B. The index automatically updates

C. A warning is logged, but the query executes

D. The query fails