What is the biggest misconception about K-Means in interviews?

The biggest misconception is that K-Means works well on all datasets if K is chosen correctly.

In reality, K selection is not the main limitation—data geometry is. Even with perfect K, K-Means fails on non-spherical, overlapping, or density-variant clusters. Many candidates incorrectly focus only on choosing K rather than validating assumptions.