Course Schedule II: A Step-by-Step Interview Walkthrough
Course Schedule II is the natural sequel to Course Schedule, and interviewers often pose it as a follow-up after you've solved the feasibility version. The shift is small to state but significant in practice: instead of asking whether all courses can be finished, it asks you to produce a valid order to finish them.
1 day ago10 min read
Course Schedule: A Step-by-Step Interview Walkthrough
Course Schedule is a problem whose entire difficulty lies in recognizing what it's actually asking. The framing — courses, prerequisites, "can you finish everything?" — sounds like a scheduling or simulation problem, and candidates who take that framing literally end up trying to build actual orderings and check them, which is both hard and slow. The candidates who pass are the ones who strip away the cover story and ask: when is it impossible to finish all courses?
2 days ago13 min read
Clone Graph: A Step-by-Step Interview Walkthrough
Clone Graph is a problem that looks like a routine traversal until you remember one thing: graphs can loop back on themselves. The interviewer is using this problem to test whether you can handle the three things that make graph copying genuinely tricky.
6 days ago11 min read
Number of Islands: A Step-by-Step Interview Walkthrough
Number of Islands is a common interview problem because it tests a specific cognitive move: can you recognize that a problem presented in one form is actually a different problem in disguise? The 2D grid framing is intuitive — humans naturally think of grids as visual things — but the underlying problem is counting connected components in a graph. The signal here is whether you can map between representations — see past the surface presentation to the underlying structure.
May 2614 min read
Path Sum in a Binary Tree: A Step-by-Step Interview Walkthrough
Path Sum in a Binary Tree is a deceptively simple-looking problem that interviewers use as a reading comprehension test as much as an algorithm test. The problem statement contains specific constraints that, if misread, lead candidates to solve a harder problem than the one asked. Candidates who skim the requirements often write algorithms that handle "any path" or "any node to any node," which is significantly more complex and earns no extra credit.
May 2511 min read
