Algorithm Speed Round - Complexity Quiz

Ten multiple-choice questions, twenty seconds each, drawn from a shuffled pool spanning complexity, dynamic programming, Monte Carlo, and pandas.

Each run deals ten questions drawn at random from a larger pool covering four topics: complexity (count the work), dynamic programming (reuse the states), Monte Carlo (price the error), and Python/pandas (move the data). Both the question order and each question's answer-choice order are shuffled every run, so repeat runs never play the same.

Every question gives you 20 seconds. A countdown runs in the corner and turns red in the last 5 seconds. Some questions include a code snippet to read. Click a choice to answer - the choice locks immediately, the correct answer is highlighted, and an explanation appears. If the clock hits zero before you answer, the question is marked as a timeout, which counts as wrong.

After each question you advance manually to the next. After the tenth, a results screen lists every question with a CORRECT or REVIEW tag, the prompt, and its explanation, so the misses become a study list. Run it again deals a fresh shuffled deck.

Why quant interviews test this

Rapid-fire technical screens - phone rounds, online assessments, the opening minutes of an onsite - test recognition speed: can you see that a snippet is quadratic, that a problem is DP-shaped, that a simulation needs 4x the paths, without warm-up time. Speed Round is a direct rehearsal for that format, including the pressure of a visible clock.

The pandas questions earn their place because quant dev and quant research interviews increasingly include data-manipulation questions alongside classic algorithms - moving a groupby or a merge through your head at speed is now table stakes.

The Speed Round guide covers how scoring works, the strategy that wins, a worked example and the mistakes most players make.

More Algorithms games

• DP Table Builder - Fill a dynamic-programming memo table cell by cell, from given base cases and a stated recurrence, until the final answer falls out.
• Mini Task - Rookie - A rookie-level coding challenge: two warm-up questions, real code run against real tests in your browser, then two wrap-up questions.
• Monte Carlo Estimator - Estimate pi by throwing random points at a circle, watch the error shrink with sample size, and internalize why halving the error always costs four times the samples.
• All Algorithms practice
• Every game guide