1Z0-184-25 Question 47
Select 2You are building a product recommendation system on Oracle Autonomous Database that stores user interaction embeddings in a VECTOR(512) column. To speed up AI-driven similarity searches, you decide to utilize OCI� vector indexing capabilities. Which two steps are required to create an efficient vector index for your table?
- A
A. Define the embeddings column using the VECTOR data type and specify the correct dimension.
- B
B. Create a standard B-tree index on the VECTOR column for quick nearest neighbor lookups.
- C
C. Transform the VECTOR values into a single numeric column and apply a range-based index.
- D
D. Use the specialized CREATE INDEX syntax to build an approximate nearest neighbor (ANN) index on the VECTOR column.
- E
E. Store embeddings externally on OCI Object Storage and reference them through a metadata column.
Show answer and explanation
Correct answers: A, D
Explanation
To perform high-performance AI-driven searches on Oracle Autonomous Database, you first define a column using the VECTOR data type with the correct dimension (in this case, 512). Next, you create an approximate nearest neighbor (ANN) index on that vector column using Oracle� specialized vector indexing. This allows the database to efficiently handle similarity or nearest neighbor queries on large sets of vector data. For more information, see Oracle� documentation regarding VECTOR data type and vector indexes, introduced in Oracle Database 23c.
- A. Correct.
A. Correct. When creating a table to store vector embeddings, you must define the column with the VECTOR data type and specify the correct dimension (e.g., VECTOR(512)) to enable vector-based operations and indexing features.
- B. Incorrect.
B. Incorrect. A standard B-tree index is not optimized for high-dimensional vector similarity searches. B-tree indexes work well for simple range or exact match queries but are not designed for approximate nearest neighbor lookup in multi-dimensional space.
- C. Incorrect.
C. Incorrect. Converting an entire embedding into a single numeric column (for instance, storing vectors as recorded distances or concatenated values) and using a range-based index is neither standard practice nor effective for large or high-dimensional embeddings. This approach would not leverage Oracle's dedicated vector search capabilities.
- D. Correct.
D. Correct. Creating a specialized approximate nearest neighbor index (e.g., an HNSW index) on the VECTOR column is required to optimize similarity searches in large, high-dimensional datasets. This type of index is specifically designed for vector-based operations.
- E. Incorrect.
E. Incorrect. Storing embeddings externally in OCI Object Storage would prevent you from using Oracle� built-in vector indexing for efficient similarity lookups. The vectors must reside in the database column for the index and queries to work properly.