Interview Practice Question Set (Phase-Independent)

Section 1: Conceptual Understanding (Big Picture)

Q1. How is Deep Learning different from traditional Machine Learning in practice, not theory?

What interviewer expects:

Answer should mention automatic feature learning, scalability with data, handling unstructured data (images, text), and higher compute requirements.

Q2. When should you NOT use Deep Learning?

What interviewer expects:

Small datasets, simple problems, need for interpretability, limited compute, strict latency constraints.

Q3. What actually happens when a model “learns”?

What interviewer expects:

Weights are updated to minimize loss using gradients; learning is optimization, not memorization.

Q4. Why is data quality more important than model complexity?

What interviewer expects:

Garbage in–garbage out, bias, noise, incorrect labels directly limit model performance.

Q5. Can a bigger model always give better accuracy? Why or why not?