1Z0-184-25 Question 82
Single answerYou are designing a product recommendation service that stores item embeddings in an Oracle Cloud Infrastructure environment. You plan to perform approximate similarity searches on these embeddings using Vector Indexes. To achieve low-latency queries at scale while still obtaining relevant results, you have decided to configure the Vector Index for approximate nearest neighbor (ANN) searches. Which step is most critical for ensuring optimal performance when creating and querying the Vector Index?
- A
A. Store embeddings in a VARCHAR2 column and rely on an automatically created B-tree index.
- B
B. Use the built-in approximate nearest neighbor index type, and tune parameters such as the number of search probes or layers based on your dataset size and query latency requirements.
- C
C. Convert embeddings to Oracle Spatial geometry objects and utilize a spatial R-tree index for approximate searches.
- D
D. Enable full-text indexing on your embeddings to benefit from approximate similarity ranking.
Show answer and explanation
Correct answer: B
Explanation
For approximate similarity searches on embeddings within Oracle Cloud Infrastructure, storing your vectors in a designated vector data type or column and using the approximate nearest neighbor (ANN) index functionality is crucial. Properly tuning parameters to control the trade-off between accuracy and search latency is a standard best practice. Refer to the Oracle documentation on Vector Search capabilities and indexing strategies for optimal performance and relevance when working with high-dimensional vector data.
- A. Incorrect.
A: Storing embeddings in a VARCHAR2 column and using a B-tree index is not suitable for similarity searches on high-dimensional vectors; B-trees are optimized for exact matching or range queries and will not provide efficient approximate nearest neighbor results.
- B. Correct.
B: Using Oracle� built-in approximate nearest neighbor index type (often configured by specifying a vector column and indexing method) and tuning parameters such as the number of probes or search depth is the recommended approach. This configuration balances performance and accuracy when searching large-scale vector data.
- C. Incorrect.
C: Converting embeddings to geometry objects and using an R-tree index targets spatial data use cases. While R-trees can handle some forms of multidimensional data, they are generally not optimized for high-dimensional similarity searches of embeddings.
- D. Incorrect.
D: Full-text indexes are meant for keyword-based matching, not vector-based similarity. Such an index would not handle the math of similarity metrics like cosine distance or Euclidean distance effectively.