Easy AI, ML & GenAI interview questions

Every LLM is one function: tokens in, a distribution over the next token out. The signal is what you say next, because the distance from that function to a useful assistant is three training stages, and almost every production complaint lands in exactly one of them.

The shift from one model per task to one model adapted many ways rewrote the job description. The interviewer is checking whether you know which half of the work got nearly free, which half did not move at all, and which problems classical ML still owns outright.

Definitions take thirty seconds. The interview is about the four things log-probs buy you in production: a confidence signal, banning tokens, guaranteeing valid output, and classification you can calibrate. Plus the tokenization gotcha that quietly breaks all of them.

The most repeated trick in prompt engineering, and the evidence for it is much thinner than the folklore. A screening question for whether you measure your prompts or inherit them from a thread of screenshots.

A warm-up that tests for the stack instinct. Interviewers look for recognizing that last-opened-first-closed maps onto a stack, plus handling the edge cases (leftover opens, early close). The pattern follows.

The textbook warm-up that tests for the hash-map instinct: swap space for time to turn O(n squared) into O(n). Interviewers look for the one-pass complement lookup. The pattern follows.

Reversing a linked list is the archetypal pointer-handling screen. What interviewers watch for: the three-pointer walk executed without ever dropping the tail of the list, plus why the iterative version wins over recursion in production. Here is the answer.

A build-it-yourself check on how learning-rate schedules are actually computed. What matters is correct closed-form formulas, knowing when each schedule shines, and why decaying the rate aids convergence. Below is the implementation of the common ones.

A prompt is code, but most teams edit it in a dashboard with no history and then cannot explain last Tuesday's quality drop. This is the small piece of infrastructure that makes prompts reviewable, pinnable, and instantly reversible.

Regression calls for different metrics than classification, and each tells its own story. What matters is knowing what RMSE vs MAE vs R² vs MAPE really measure and their failure modes around outliers, scale, and zeros.

A soft-sounding question that quietly filters 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 prompt that tests self-awareness and honesty, not a polished non-answer. What scores is a genuine weakness plus the work you're putting in to fix it. Here is how to answer it well.

The opener in half of all Applied AI screens, and most candidates answer it with a definition nobody scores. The distinction that actually earns points is about where you start and what your bottleneck turns out to be.

The classic closer, and most candidates answer it with a horoscope or a list of tool names. The signal is whether you hold an actual view, can justify it from what is already happening, and know what it means for the job.