Largest Rectangle in Histogram earns its status as a hard problem because while the brute force solution is obvious, the optimization is not, and the leap between them requires a genuinely clever data structure: the monotonic stack.

Median of Two Sorted Arrays is one of the most famous Hard problems in the interview canon, and its reputation is earned by a single, brutal constraint: the required time complexity is O(log(m + n)). Without that constraint, the problem is trivial — merge the two arrays and grab the middle. With it, you're forced into a binary search, except there's no obvious single sorted array to binary-search over.

Minimum Window Substring is the problem that separates people who've used sliding windows from people who understand them. Most candidates have seen a window expand and contract on simpler problems, but this one forces two harder questions at once: how do you efficiently check whether the current window contains everything you need (including duplicates), and how do you know when to expand versus when to shrink?

Minimum Spanning Tree problems are a staple of technical interviews because they sit at the intersection of greedy reasoning and the Union-Find data structure — two things interviewers love to test together. The signal here is whether you understand greedy correctness and can apply Union-Find fluently, not just whether you can recite the steps.

Cheapest Flights Within K Stops is a problem that looks like a routine shortest-path question, but it quietly adds a twist that breaks the standard tool. The signal interviewers want is whether you can recognize when a familiar algorithm's assumptions are violated and reach for the right variant instead of forcing the wrong tool.

Critical Connections in a Network is a genuinely hard graph problem, the kind where knowing the right algorithm matters more than coding speed. It asks you to find every bridge in a graph — an edge whose removal would split the network into disconnected pieces. It's a problem that rewards understanding the theory, not just pattern-matching.

Graph Valid Tree looks like a quick traversal problem and turns out to be a test of whether you actually know what a tree is. Most candidates can run a DFS, but this problem rewards understanding the structural definition of a tree — connected and acyclic — and recognizing that those two conditions, combined with the right invariant, collapse into a surprisingly clean check. The signal here is whether you can reason about graph properties rather than just running an algorithm

Alien Dictionary is one of the hardest "medium" problems in the interview rotation, and it earns that reputation by hiding a clean graph problem behind a deceptive premise. The instinct is to think about sorting or string comparison — but those are dead ends. The real insight is that the sorted order of the words is a set of ordering constraints between letters, and recovering the alphabet means assembling those constraints into a consistent sequence: a topological sort.

Reconstruct Itinerary is a problem that looks like a routine graph traversal and turns out to be a subtle trap for the obvious approach. The setup practically begs for backtracking. That works, but it's exponential, and the candidates who reach for it spend their time wrestling with backtracking bookkeeping instead of seeing the structure. The candidates who recognize "use every edge exactly once" as the definition of an Eulerian path unlock a clean, near-linear algorithm.

Network Delay Time is the problem interviewers use to find out whether you understand why Dijkstra's algorithm exists, not just how to type it out. The candidates who understand that unequal edge weights invalidate BFS's core assumption know to reach for Dijkstra instead. The signal here is whether you can tell weighted shortest path from unweighted shortest path, and whether you know why the distinction forces a different algorithm.

Minimum Height Trees is a problem that punishes the obvious approach and rewards stepping back to find structure. The naive reading — "try every node as the root, measure the height, keep the best" — works, but it's quadratic, and the interviewer is specifically watching to see whether you settle for that or push for the insight that makes it linear. The key realization is that this isn't really a "measure heights" problem at all; it's a "find the center of the tree" problem

Word Ladder is a shortest-path problem wearing a string-manipulation costume, and the entire challenge is seeing through the disguise. The problem talks about transforming words one letter at a time, which sounds like it might call for clever string algorithms or backtracking. But the moment you reframe words as nodes and one-letter transformations as edges, it becomes a textbook shortest-path-in-an-unweighted-graph problem.

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.

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?

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.

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.

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.

Binary Tree Maximum Path Sum is one of the genuinely hard tree problems in the standard interview rotation. Most tree problems are about traversing a structure or computing a single value per node. This one asks something more subtle: find the best path anywhere in the tree, where "anywhere" includes paths that don't start at the root, don't end at a leaf, and bend through any node.

Serialize and Deserialize Binary Tree is one of the rare interview problems that's actually a design problem. There's no fixed answer — you get to choose your own encoding, and the interviewer is watching to see whether your choice is principled or arbitrary. The signal here is whether you can think about data as having structure that needs explicit encoding, not just values that get written down.

Lowest Common Ancestor of a BST is a problem that interviewers use to test a specific kind of reading comprehension: did the candidate notice they were handed a BST, or did they treat it as a generic binary tree? The signal is whether you treat the data structure's invariants as algorithmic information rather than incidental detail.