1Z0-184-25 Question 75
Single answerYour data science team has developed a product recommendation engine on Oracle Cloud Infrastructure, storing product embedding vectors in an Autonomous Database. You need to perform an exact similarity search to find the most similar products based on these vectors, ensuring no approximations. Which approach should you implement to achieve this requirement?
- A
Store embeddings in Object Storage and use a custom script in Oracle Functions to compare each vector sequentially.
- B
Use the Autonomous Database Vector Search capability with a dedicated index configured for exact matching.
- C
Rely on approximate nearest neighbor algorithms with partial vector comparisons for faster results.
- D
Implement a Full Text Index on each product's metadata and perform a search with text-based similarity operators.
Show answer and explanation
Correct answer: B
Explanation
To perform an exact vector similarity search in Oracle Cloud Infrastructure, you should leverage the Autonomous Database's built-in vector search capability, which allows you to create an index specifically on vector columns. This type of indexing ensures that your queries return precisely the most similar vectors. Refer to Oracle� documentation on Vector Search Indexes within Autonomous Database for guidance on proper configuration.
- A. Incorrect.
Storing vectors in Object Storage and writing a custom script would be slow and resource-intensive for large datasets. This approach lacks an efficient indexing mechanism for exact similarity queries.
- B. Correct.
This is the correct answer. Oracle Autonomous Database provides a vector search capability with indexing that supports exact matching, ensuring no approximation and delivering precise similarities. It indexes vector columns properly, so queries can quickly return exact nearest neighbors.
- C. Incorrect.
Although approximate nearest neighbor algorithms can improve performance for massive datasets, they introduce some level of inaccuracy. The question explicitly requires an exact search with no approximations, making this approach unsuitable.
- D. Incorrect.
Full Text Indexes are intended for textual data and keyword searches, not for exact numeric (vector) similarity. They do not provide a true vector-based comparison required by the scenario.