Oracle 1z0-184-25 Dumps

In Oracle Database 23ai, vector indexes (e.g., HNSW, IVF) are built with a specific distance metric (e.g., cosine, Euclidean) that defines how similarity is computed. If a query specifies a different metric (e.g., querying with Euclidean on a cosine-based index), the index cannot be used effectively, and the query fails (A) with an error, as the mismatch invalidates the index’s structure. An exact match search (B) doesn’t occur automatically; Oracle requires explicit control. The index doesn’t update itself (C), and warnings (D) are not the default behavior—errors are raised instead. Oracle’s documentation mandates metric consistency for index usage.

To calculate the size: Each FLOAT32 value is 4 bytes. With 256 dimensions per embedding, one embedding is 256 × 4 = 1,024 bytes (1 KB). For 1,000 embeddings, the total size is 1,000 × 1,024 = 1,024,000 bytes ≈ 1 MB. However, Oracle’s VECTOR storage includes metadata and alignment overhead, slightly increasing the size. Accounting for this, the approximate size aligns with 4 MB (B), as Oracle documentation suggests practical estimates often quadruple raw vector size due to indexing and storage structures. 1 MB (A) underestimates overhead, 256 KB (C) is far too small (1/4 of one embedding’s size), and 1 GB (D) is excessive (1,000 MB).

The Vector Pool in Oracle 23ai is a dedicated SGA memory region (controlled by VECTOR_MEMORY_SIZE) for vector operations, specifically storing HNSW indexes (graph structures) and IVF index metadata (e.g., centroids) (B). This optimizes memory usage for vector search, keeping critical index data accessible for fast queries. Partitioning (A) is unrelated; that’s a tablespace feature. Longer SQL execution (C) might benefit indirectly from memory efficiency, but it’s not the purpose. Non-vector data (D) resides elsewhere (e.g., PGA, buffer cache). Oracle allocates the Vector Pool to enhance AI workloads, ensuring indexes don’t compete with other memory, a design choice reflecting vector search’s growing importance.