1Z0-184-25 Question 73
Single answerYour company is building a content-based recommendation engine on Oracle Cloud Infrastructure. You store product feature embeddings in an Autonomous Database table using the built-in vector data type. The team wants to ensure that all similarity queries return only exact matches, without approximations. Which approach will guarantee exact similarity searches for this scenario?
- A
Create an HNSW index on the vector column and run a k-NN query for exact results.
- B
Build a standard text index on each embedding column and use a CONTAINS clause to match vectors.
- C
Use the table� vector column without an approximate index and perform direct distance calculations within your SQL query conditions.
- D
Rely on the default B-tree index on the vector column by simply adding an INDEX clause at table creation.
Show answer and explanation
Correct answer: C
Explanation
Exact similarity search requires comparing all vectors in the dataset without relying on approximate indexing structures like HNSW. In Oracle Autonomous Database with the vector data type, you can perform these comparisons directly in your SQL query by using built-in distance functions or operators on the vector column. This ensures complete accuracy at the cost of higher computational overhead. For more details, refer to the Oracle documentation on vector data types and similarity queries in Autonomous Database.
- A. Incorrect.
Option 1 is incorrect. HNSW indexes provide approximate nearest neighbor search, which speeds up queries but does not guarantee exact results. They trade some accuracy for performance.
- B. Incorrect.
Option 2 is incorrect. Text indexes (CONTAINS clause) are designed for text-based queries and do not support vector similarity search. They will not correctly operate on numeric embeddings.
- C. Correct.
Option 3 is correct. Performing direct distance calculations (e.g., Euclidean or cosine similarity) on the vector column without an approximate index ensures exact matching. This approach returns complete and accurate results for every query, although it may require more computation.
- D. Incorrect.
Option 4 is incorrect. B-tree indexes cannot directly index high-dimensional vector data. Simply adding an INDEX clause on a vector column won�t enable correct vector similarity queries.