Most interview prep gives you a question and one “correct” answer. That’s not how real interviews work — the gap between getting the offer and getting passed over is rarely about knowing the topic, it’s about depth, tradeoffs, and judgment.

For each of the 12 questions in this bank, you get three answers side by side:

Junior-level answer — correct, but shallow. Enough to survive, rarely enough to stand out.
Senior-level answer — same question, with the depth, tradeoffs, and real-world grounding that signals someone who’s actually shipped ML systems.
Common wrong answer (and why it fails) — a version that sounds confident and even partly right, but contains the exact mistake experienced interviewers quietly flag.

Covers core ML theory (bias-variance, regularization, attention mechanisms, and more), applied/systems questions (data leakage, model evaluation before production), and behavioral questions — with a practice framework at the end so you’re not just reading, you’re training.