Greedy Interview Questions & Solutions

Greedy algorithms make the locally optimal choice at each step and hope it leads to a globally optimal answer — and the whole interview challenge is knowing when that hope is actually justified. When greedy works, the solution is beautifully short; when it doesn't, it fails silently on a tricky case. So the real skill these problems build isn't coding, it's the judgment to recognize a problem with the "greedy choice property" and the ability to argue, out loud, why the local choice is safe.

These solutions cover the interview staples: interval scheduling and merging, the jump-game family, assigning resources to maximize or minimize a total, and problems where sorting first exposes the greedy order. For each one we spell out the exchange argument — the reasoning for why you never regret the greedy pick — because "it passed the examples" won't satisfy an interviewer. You'll also see a few problems that look greedy but aren't, so you learn to sanity-check the approach before committing. That instinct for when greedy is and isn't safe is exactly what separates a lucky guess from a defensible solution.