Oracle AI Vector Search Professional 1z0-184-25 Question # 9 Topic 1 Discussion
1z0-184-25 Exam Topic 1 Question 9 Discussion:
Question #: 9
Topic #: 1
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.
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
In Oracle Database 23ai, vector search accuracy hinges on the consistency of the embedding model. The VECTOR data type stores embeddings as fixed-dimensional arrays, and similarity searches (e.g., using VECTOR_DISTANCE) assume that all vectors—stored and query—are generated by the same model. This ensures they occupy the same semantic space, making distance calculations meaningful. Regular updates (B) maintain data freshness, but if the model changes, integrity is compromised unless all embeddings are regenerated consistently. The distance algorithm (C) (e.g., cosine, Euclidean) defines how similarity is measured but relies on consistent embeddings; an incorrect model mismatch undermines any algorithm. Physical storage location (D) affects performance, not integrity. Oracle’s documentation stresses model consistency as a prerequisite for reliable vector search within its native capabilities.
[Reference:Oracle Database 23ai AI Vector Search Guide, Chapter on Vector Search Prerequisites., , ]
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