1Z0-184-25 Question 160
Single answerYour company manages a massive collection of image and text embeddings on Exadata AI Storage, and your team is implementing real-time vector similarity queries to power personalized recommendations. You want to minimize data transfer between the database compute and storage tiers while reducing query latency. Which approach should you implement to leverage Exadata AI Storage for accelerating your vector searches?
- A
Enable Hybrid Columnar Compression (HCC) for embedding columns to reduce storage space.
- B
Create an Approximate Nearest Neighbor (ANN) index on the embedding columns and ensure vector processing is pushed down to the Exadata AI Storage layer.
- C
Implement partition pruning on the embeddings table to isolate relevant data segments.
- D
Use the In-Memory Column Store for all embedding columns.
Show answer and explanation
Correct answer: B
Explanation
Pushing vector search computations to the Exadata AI Storage layer helps minimize data transfer and fully exploit Exadata� offload capabilities. By creating an Approximate Nearest Neighbor index on the embeddings and leveraging Exadata pushdown processing, you reduce query latency and offload the heavy lifting from database nodes to specialized Exadata hardware. For more details, refer to Oracle� documentation on vector data indexing and Exadata offloading best practices.
- A. Incorrect.
Option 1: Although HCC is useful for reducing storage and I/O, it does not specifically address or accelerate vector search logic. HCC primarily compresses data on disk, which may lower storage costs, but it won�t optimize the actual similarity computations that happen with vector queries.
- B. Correct.
Option 2 (Correct): Creating an ANN index and pushing down vector similarity calculations to Exadata AI Storage is key to improving performance. The ANN index narrows down candidates for similarity matching, and offloading vector computations to the storage tier reduces data movement and leverages the specialized processing capabilities in Exadata AI Storage.
- C. Incorrect.
Option 3: Partition pruning is a useful technique to reduce query scope, but it does not address fine-grained vector processing or substantially accelerate vector similarity calculations. While it can help with data management, it is not the primary method to optimize real-time vector queries.
- D. Incorrect.
Option 4: The In-Memory Column Store can speed up certain analytical queries, but vector similarity searches benefit more from specialized indexing and pushdown capabilities. Using In-Memory alone does not provide the same targeted acceleration for vector computations that ANN indexing with Exadata AI Storage offload can deliver.