Minimum Time to Complete All Tasks
A hard-tier problem at 38% community acceptance, tagged with Array, Binary Search, Stack. Reported in interviews at Snap and 0 others.
Minimum Time to Complete All Tasks is a hard problem that's appeared at Snap and sits at a 38% acceptance rate. You're given tasks with start windows, end windows, and durations, and you need to find the minimum time to schedule them all. This isn't a straightforward greedy problem, which is where most candidates get stuck. The trick lives in how you order tasks and decide when to pack processing time into their available windows. If you hit this problem cold in an OA, the greedy insight isn't obvious. StealthCoder surfaces the working approach in seconds, invisible to the proctor.
Companies that ask "Minimum Time to Complete All Tasks"
Minimum Time to Complete All Tasks is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made for the engineer who has done the work but might still blank with a webcam pointed at him.
Get StealthCoderThe core insight is greedy with a twist: sort tasks by end time, then process them backward through your time window. For each task, you try to fit its duration as late as possible within its valid window. If you can't fit it in the remaining time, you know the problem's unsolvable. Stack and binary search enter when you need to track occupied intervals and check whether a task can be scheduled efficiently. Most candidates either attempt a naive DP approach or miss the backward-greedy ordering entirely. The trick is recognizing that processing from the deadline backward minimizes conflicts and lets you pack tasks into the tightest possible schedule. If this pattern doesn't click during the OA and your brute force times out, StealthCoder gives you the correct greedy + stack solution before you run out of time.
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Minimum Time to Complete All Tasks recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made for the engineer who has done the work but might still blank with a webcam pointed at him. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Minimum Time to Complete All Tasks interview FAQ
Is this still asked at Snap or other top companies?+
It's confirmed at Snap. The problem isn't highly frequent across the broader industry based on available reports, so it's not a mainstream drill. That's exactly why it can blindside you in an OA. Prep the pattern so you're not guessing.
What's the real trick here? Greedy sorting?+
Greedy sorting by end time is necessary but not sufficient. The trick is scheduling each task as late as possible within its window, using a stack to track occupied intervals. Most solutions fail because they sort correctly but then schedule tasks forward instead of backward, creating unnecessary conflicts.
How does binary search fit in?+
Binary search lets you efficiently check whether you can fit a task's duration into the remaining available time slots. Without it, checking feasibility for each task becomes O(n^2) or worse. It's the optimization that makes the solution run on time.
Why do so many people fail this problem?+
The 38% acceptance rate reflects the non-obvious scheduling insight. Candidates often assume a simple greedy sort works, or they jump to DP without spotting the greedy pattern. The backward-processing step is counterintuitive and requires working through an example to see why it's necessary.
Is there a DP solution instead of greedy?+
DP is possible but far less efficient than the greedy approach. Greedy with interval tracking runs in O(n log n) and is clean. DP solutions often hit memory limits or time out. The problem design pushes you toward greedy, so that's what you should drill.
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