1Z0-184-25 Question 40
Select 2You are designing an image search feature for a media platform running on OCI. Users frequently upload and search through millions of images, each represented as a 512-dimensional embedding stored in your Autonomous Database. You need low-latency queries for real-time recommendations, and you expect incremental uploads of new embeddings daily. Which two actions should you take to ensure efficient vector indexing and similarity queries in this environment?
- A
Adopt approximate nearest neighbor indexing to handle large-scale data and improve query latency.
- B
Create one large monolithic vector index without partitioning to simplify index maintenance.
- C
Schedule incremental index synchronization after batch inserts instead of continuously updating the index on every single row insertion.
- D
Use a compressed version of every embedding to reduce storage capacity, then rely on full-precision vector search for accuracy.
Show answer and explanation
Correct answers: A, C
Explanation
In Oracle Autonomous Database, enabling approximate nearest neighbor indexing allows you to support real-time image similarity searches at scale. A balanced approach to index maintenance�such as scheduling incremental synchronization after batch inserts�keeps operational overhead manageable while ensuring the index remains up to date. For more details, refer to Oracle� documentation on vector indexing best practices and performance tuning guidelines in Autonomous Database.
- A. Correct.
Option 1: Correct. Approximate nearest neighbor indexing can greatly reduce query latency when dealing with high-dimensional data. It� a recommended approach for large-scale similarity queries because exact methods can be too slow for millions of vectors.
- B. Incorrect.
Option 2: Incorrect. Relying on a single monolithic index for an extremely large dataset often leads to higher maintenance overhead and slower query times. Partitioning or segmenting the index helps manage performance and scalability.
- C. Correct.
Option 3: Correct. Performing index synchronizations after controlled batch inserts helps maintain index freshness without the overhead of immediate updates for every single row. This approach balances performance with operational convenience.
- D. Incorrect.
Option 4: Incorrect. While compression can save storage, it can also degrade the precision of the underlying vector data and potentially slow down similarity searches. When optimizing for query performance, preserving the original dimensional data is generally preferred over compression.