Advanced Deep Learning Interview Questions #6 - The Linear Separability Trap

Throwing more data at a non-linearly separable problem fails because the model class itself cannot represent the required decision boundary.

You’re in a Senior ML Engineer interview at Stripe and the interviewer asks:

“Your fraud detection model, a simple linear perceptron, is catching isolated anomalies but completely missing coordinated attacks. Feature A looks safe on its own, and Feature B looks safe on its own, but combined, they scream fraud. A junior engineer suggests throwing 10x more…