Easy Applied AI interview questions

A warm-up that screens for the stack instinct. The signal is recognizing last-opened-first-closed maps to a stack, and handling the edge cases (leftover opens, early close). Here is the pattern.

The canonical warm-up that screens for the hash-map instinct: trade space for time to turn O(n squared) into O(n). The signal is the one-pass complement lookup. Here is the pattern.

Reversing a linked list is the canonical pointer-handling screen. The signal is the three-pointer walk done without ever losing the rest of the list, plus knowing why the iterative version beats recursion in production. Here is the answer.

A from-scratch test of how learning-rate schedules are actually computed. The signal is correct closed-form formulas, knowing when each schedule shines, and why decaying the rate helps convergence. Here is the implementation of the common ones.

Regression needs different metrics than classification, and each tells a different story. The signal is knowing what RMSE vs MAE vs R² vs MAPE actually measure and their failure modes around outliers, scale, and zeros.

A soft-sounding question that quietly screens out low-effort candidates and tests real fit. The signal is specific, researched reasons tied to your trajectory, not flattery. Here is the structure that lands.

The classic question that screens for self-awareness and honesty, not for a perfect non-answer. The signal is a real weakness plus what you're actively doing about it. Here is how to answer it well.