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1Z0-184-25
90 Minutes
60
Oracle Database AI Vector Search Professional
A: Support for legacy SQL*Plus clients
B: Improved accuracy compared to external models
C: Reduced embedding dimensions for faster processing
D: Enhanced security because data remains within the database
A: Exact similarity search using flat search
B: Approximate similarity search with a low target accuracy setting
C: Relational filtering combined with an exact search
D: Exact similarity search with a high target accuracy setting
A: It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries
B: It primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead
C: It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing reliance on external AI providers
D: It enables Large Language Models (LLMs) to access and process real-time data streams from diverse sources to generate the most up-to-date insights
A: Using the same embedding model for both vector creation and similarity search
B: Regularly updating vector embeddings to reflect changes in the source data
C: The specific distance algorithm employed for vector comparisons
D: The physical storage location of the vector data
A: SELECT
B: UPDATE
C: DELETE
D: JOIN ON VECTOR columns
