1Z0-184-25 試験問題を無料オンラインアクセス

A database administrator wants to change the VECTOR_MEMORY_SIZE parameter for a pluggable database (PDB) in Oracle Database 23ai. Which SQL command is correct?

• A.ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
• B.ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR
• C.ALTER SYSTEM RESET VECTOR_MEMORY_SIZE
• D.ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH

You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?

• A.The specific distance algorithm employed for vector comparisons
• B.Using the same embedding model for both vector creation and similarity search
• C.Regularly updating vector embeddings to reflect changes in the source data
• D.The physical storage location of the vector data

An application needs to fetch the top-3 matching sentences from a dataset of books while ensuring a balance between speed and accuracy. Which query structure should you use?

• A.Approximate similarity search with the VECTOR_DISTANCE function
• B.A combination of relational filters and similarity search
• C.Multivector similarity search with approximate fetching and target accuracy
• D.Exact similarity search with Euclidean distance

Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?

• A.Using a vector with a data type that is not supported by the function
• B.Providing a vector with duplicate values for its components
• C.Providing a vector with a dimensionality that exceeds the specified dimension count
• D.Calling the function on a vector that has been created with TO_VECTOR()

When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?

• A.The choice of programming language used to process the dataset (e.g., Python, Java)
• B.The storage format of the new dataset (e.g., CSV, JSON)
• C.The physical location where the vector embeddings are stored
• D.The same vector embedding model must be used for vectorizing the data and creating a query vector