1Z0-184-25 Question 116
Single answerYou are designing a product recommendation engine for a global e-commerce platform. The development team wants to store machine learning-generated embeddings in an Oracle Database so they can quickly run similarity searches (e.g., 'nearest neighbor' queries) on product descriptions. They plan to use Oracle Database 23c, which supports vector data. Which approach is recommended to properly store and index these embeddings for fast vector-based similarity searches?
- A
Create a table with a VARCHAR2 column to store each numerical component of an embedding as a comma-separated string
- B
Leverage Oracle Database 23c� built-in vector data type and create a vector index to optimize similarity queries
- C
Use an unstructured XML column and manually parse numeric values for similarity calculations
- D
Store embeddings in separate NUMBER columns for each dimension, then create a B-tree index on those columns
Show answer and explanation
Correct answer: B
Explanation
In Oracle Database 23c, the built-in vector data type and index provide specialized support for storing and querying embeddings. This native functionality is designed to optimize nearest neighbor searches by organizing vectors efficiently. For more details, refer to the official Oracle Database 23c documentation on vector data management and indexing.
- A. Incorrect.
Storing vectors as comma-separated strings in VARCHAR2 columns forces manual parsing and does not leverage native vector features for efficient similarity searches. This approach is not optimal.
- B. Correct.
Oracle Database 23c introduces a native vector data type and indexing capabilities specifically designed for vector embeddings. Creating a table column of this vector type and building the associated index is the recommended method to achieve fast vector-based queries.
- C. Incorrect.
XML-based storage requires significant overhead for parsing and does not offer built-in optimization for vector similarity. This would be cumbersome and slow for large-scale similarity searches.
- D. Incorrect.
While storing each dimension in a separate NUMBER column is possible, using B-tree indexes on numerous numeric columns is inefficient for vector similarity queries. This approach does not leverage Oracle 23c's built-in vector indexing.