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?

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.

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.

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.

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.

Learn how to solve the Subsets coding problem to prepare for your next technical interview! The Subsets problem tests whether you understand how to explore a decision tree without missing cases or duplicating work. It's a classic interview question because the same thinking shows up across backtracking, bit manipulation, and combinatorics problems.

Learn how to solve the Permutations coding problem to prepare for your next technical interview! Interviewers use it to see whether you can reason about recursion, backtracking, and state management, and whether you can do it without losing track of where you are.

Learn how to solve the Non-Overlapping Intervals coding problem to prepare for your next technical interview!

"Merge Intervals" is one of those classic algorithm problems that shows up frequently in technical interviews. It's a great test of your...

Jump Game II is a classic follow-up to the original Jump Game problem. It’s not just about whether you can reach the end... now you have to do it in the fewest jumps possible! That small change turns a simple reachability problem into one that tests how well you can optimize greedy strategies or dynamic programming under pressure.

The "Jump Game" question is a popular one for interviews because it tests your ability to think greedily and work with dynamic movement through an array. It's a great warm-up for range-based greedy logic and helps build intuition for reachability problems, concepts that show up often in competitive coding and systems design.

The "Gas Station" problem is one of those deceptively simple problems that really tests your understanding of greedy strategies and circular arrays. It shows up frequently in interviews because it blends math, algorithmic reasoning, and a need for linear-time optimization. Whether you're optimizing a delivery route or designing a resource allocation system, this kind of thinking comes in handy.

"Candy" is one of those deceptively simple problems that tests your ability to think through constraints and find a clean, efficient strategy. The "Candy" problem is a classic greedy algorithm challenge often asked in interviews. It tests whether you can distribute limited resources while meeting strict conditions on relative ranking — something that comes up surprisingly often in real-world systems.

"Assign Cookies" is a problem that is a simple yet elegant introduction to greedy algorithms. It asks us to match two lists - one representing the greed of children and the other representing cookie sizes - in a way that maximizes happiness. It’s a great warm-up to practice sorting and thinking about matching strategies in real-world-like scenarios.

The "Longest Repeating Character Replacement" question is one of those string manipulation problems that sneak up on you. At first glance, it seems like a counting or brute-force challenge, but the optimal solution leans heavily on sliding window and frequency analysis. It’s a fantastic warm-up for mastering problems involving substrings and character frequency tracking, common themes in many real-world parsing and formatting systems.

The Minimum Size Subarray Sum problem is a great example of using the sliding window technique to solve a real-world scenario - finding the smallest set of contiguous actions or values that meet a threshold. It helps build your skills with optimizing time and space over brute-force solutions and comes up frequently in interviews at all levels.

The Longest Substring Without Repeating Characters problem is one of those warm-up problems that seems deceptively simple but teaches you a ton about sliding window techniques and string manipulation. It shows up frequently in interviews and also mimics real-world cases like processing tokens or user input where uniqueness matters.

Maximum Subarray - Kadane's Algorithm is is a classic problem that introduces one of the most elegant uses of dynamic programming. It teaches you how to keep track of optimal sub-solutions and make decisions based on current and previous results. Once you’ve got this down, it opens the door to more complex dynamic programming challenges.