1Z0-184-25 Question 48
Single answerYou are designing a recommendation system on Oracle Cloud Infrastructure where millions of product embedding vectors (each 256-dimensional) are stored in an Oracle Database table. Queries to find the top-k similar items by cosine distance are taking too long with a simple table scan. Which method provides the most efficient way to speed up these AI vector similarity searches while ensuring the data is still easily updated?
- A
Create a specialized approximate nearest neighbor (ANN) index on the vector column using Oracle� built-in vector index type.
- B
Add a standard B-tree index on the numeric columns representing the vector values.
- C
Convert each vector to a JSON document and index it using Oracle JSON functional indexes.
- D
Use an external text index (Oracle Text) to index the vector data and search with keyword-based queries.
Show answer and explanation
Correct answer: A
Explanation
In Oracle Database (including the latest releases running on OCI), you can store machine learning embedding data in a vector column and create a dedicated approximate nearest neighbor (ANN) index for efficient similarity searches. This specialized index type dramatically reduces query times for large-scale AI vector data while supporting common operations like cosine or Euclidean distance computations. For more details, refer to Oracle Database documentation on vector data types and ANN indexes.
- A. Correct.
Correct: Oracle Database offers a built-in vector index type (ANN index) for columns with vector data. This specialized index accelerates nearest neighbor searches by reducing latency for high-dimensional data and is designed for AI/ML workloads.
- B. Incorrect.
Incorrect: A B-tree index is not efficient for multi-dimensional vector similarity searches. It works well for equality or range queries on single columns, but does not handle high-dimensional data effectively.
- C. Incorrect.
Incorrect: While Oracle JSON functional indexes can help with certain JSON queries, converting vectors to JSON structures does not natively optimize for cosine distance queries. This approach would still be inefficient compared to a dedicated vector index.
- D. Incorrect.
Incorrect: Oracle Text indexes are designed for text-based searches (using keywords, lexical analysis, etc.), not for numerical vector distance computations. This would not significantly improve similarity search performance for embedding vectors.