Minimize Tokens Remaining
Reported by candidates from Microsoft's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Microsoft asked this in February 2025, and it's a greedy problem dressed up as an optimization puzzle. You're given an array of token values and a starting score. You trade tokens for points or points for tokens, aiming to maximize your score while minimizing leftover tokens. The pattern is straightforward once you see it: sort, then use two pointers to decide when to spend small tokens (gain points) and when to burn points to unlock big tokens. StealthCoder will have the two-pointer skeleton ready if you blank on the trade-off logic.
Pattern and pitfall
Sort the array. Use left and right pointers. Greedily consume the cheapest token (left) when you have enough score to gain a point. When you can't afford another cheap token but you have points and there are expensive tokens left, burn points to unlock the most expensive one (right). The key insight: you want maximum score first, then minimize remaining tokens as a tiebreaker. Most candidates overthink this and try dynamic programming or brute force. The greedy approach is O(n log n) due to sorting. The common trap is not resetting pointers correctly or burning points when you shouldn't. StealthCoder handles the pointer dance during the live OA.
StealthCoder is the hedge for the one pattern you didn't drill. It runs invisibly during the screen share.
You can drill Minimize Tokens Remaining cold, or you can hedge it. StealthCoder runs invisibly during screen share and surfaces a working solution in under 2 seconds. The proctor sees the IDE. They don't see what's behind it. If you're reading this with an OA window open, you're who this was built for.
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This OA pattern shows up on LeetCode as minimize the difference between target and chosen elements. If you have time before the OA, drill that.
You've seen the question.
Make sure you actually pass Microsoft's OA.
Microsoft reuses patterns across OAs. If you're reading this with an OA window open, you're who this was built for. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Minimize Tokens Remaining FAQ
Is this really a greedy problem?+
Yes. Sort once, then two pointers. Greedy works because buying the cheapest token maximizes your score per transaction, and selling the most expensive token gives you the most points back. No DP needed. This is a classic greedy-as-obvious-in-hindsight problem.
What's the trick Microsoft is testing?+
Can you spot that you should buy low and sell high using two pointers, not brute force. Also, edge case: you must have enough score to buy a token. If score is 0, you can't buy anything. The problem rewards clean pointer logic and boundary handling.
How long should this take in the real OA?+
15-20 minutes if you see the pattern. Sort, set up two pointers, write the while loop. Test on the given examples. The hard part is not overthinking it. If you're past 25 minutes and still designing, you've gone wrong.
Will Microsoft test follow-ups on this?+
Probably. They might ask for time or space optimization (you're already O(n log n) time and O(1) space after sort). Or: what if tokens repeat. Stays the same. Or: what if score starts negative. You lose. Greedy holds.
What if I blank on the trade-off logic live?+
That's what StealthCoder is for. It reads the problem during the OA and will show you the two-pointer skeleton, the sort, and the buy/sell conditions. You paste the logic, debug, and move on. No need to reinvent the wheel under time pressure.