Min Cars to Remove
Reported by candidates from Microsoft's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
You've got a Microsoft OA in your inbox and "Min Cars to Remove" is on it. April 2025 candidates are seeing this one, and it's a greedy-plus-array problem that trips up people who over-think the state management. The core ask is simple: figure out the minimum number of cars to remove from a sequence so that what remains satisfies some property. You need to recognize that you're building the longest valid subsequence, not rearranging. StealthCoder will catch you if you blank on the greedy choice.
Pattern and pitfall
This is a greedy problem dressed up as optimization. You iterate through the cars (or whatever sequence is given), and at each step you decide: keep or remove. The trick is that keeping a car is only valid if it maintains the constraint. Most candidates try to backtrack or use DP when greedy works: scan left to right, keep every car that doesn't break the rule, count what you removed. The constraint itself (often about car weights, speeds, or order) is the real pivot. If you can't articulate the greedy choice in 30 seconds, you're going to struggle on the live OA. StealthCoder exists to feed you that choice the moment you hesitate.
The honest play: practice the pattern, and have StealthCoder ready for the one you didn't see coming.
You can drill Min Cars to Remove 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. Built for the candidate who saw this exact problem leak two days before his OA and wondered if anyone had a play.
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Microsoft reuses patterns across OAs. Built for the candidate who saw this exact problem leak two days before his OA and wondered if anyone had a play. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Min Cars to Remove FAQ
Is this a DP problem or greedy?+
Greedy. You're scanning and keeping cars that fit the constraint. DP is overkill. The moment you realize you want to track 'longest valid subsequence ending at i', switch to greedy: keep cars in order if they don't violate the rule.
What's the constraint that makes a car invalid?+
The problem statement will spell it out. Common ones: weights must be non-decreasing, speeds must be increasing, or some physical rule (like a car can't remove another car). Read it three times before coding.
Can I rearrange the cars?+
No. You remove cars from the sequence as-is. Order matters. That's why greedy works: you iterate once, left to right, and decide to keep or discard each car.
What if multiple cars can be removed?+
You remove the minimum number. Greedy finds that by keeping every car that's valid given the cars you've already kept. Count total minus kept.
How do I verify my solution works?+
Trace through the example manually. Keep a list of kept cars. For each new car, check if adding it violates the constraint. If yes, remove it. If no, add it. Count removals. If it matches the expected output, you're good.