What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity...

Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., ∑(xi - yi)² vs. √∑(xi - yi)². Since the square root is monotonic, ranking order remains identical, but avoiding it (C) reduces computational cost, making queries faster—crucial for large-scale vector search. It’s not the default metric (A); cosine is often default in Oracle 23ai. It doesn’t relate to partitioning (B), an indexing feature. Accuracy (D) is equivalent, as rankings are preserved. Oracle’s documentation notes L2-squared as an optimization for performance.