Maximum Shown Segments
Reported by candidates from TikTok's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
TikTok's April 2024 assessment included a segment optimization problem that looks deceptively straightforward until you hit the edge cases. You're likely maximizing something across a range or set of segments, and the trap is assuming a greedy or linear approach works. The real solution involves either dynamic programming or a careful greedy choice with proper state tracking. StealthCoder can catch the pattern in seconds if you freeze during the OA, but understanding the core idea now means you won't need it.
Pattern and pitfall
Segment problems at TikTok typically hinge on interval overlap, selection, or arrangement. 'Maximum shown' suggests you're selecting or ordering segments to maximize a count or value. The common pitfall is greedy-by-size or greedy-by-start without considering the global constraint. The trick is often that you need to track state (which segments are active, how many fit), and that state determines what you can show next. DP solutions iterate through segments and compute the max at each point. A correct greedy approach requires sorting by endpoint and checking compatibility carefully. StealthCoder reads the full constraint set and applies the pattern instantly, which is your safety net if the logic tangles during the live OA.
Drill it cold or hedge it with StealthCoder. Either way, don't walk into the OA hoping you remember the trick.
You can drill Maximum Shown Segments 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. Made for the candidate who got the OA invite this morning and has 72 hours, not six months.
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TikTok reuses patterns across OAs. Made for the candidate who got the OA invite this morning and has 72 hours, not six months. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Shown Segments FAQ
Is this a classic interval scheduling problem?+
Likely yes, but 'maximum shown' adds a twist. You're probably not just counting non-overlapping intervals. The goal might be to maximize count, value, or display time. Check if segments can overlap, if they have weights, or if there's a display order constraint. That detail changes the algorithm entirely.
Should I sort and then greedily pick?+
Maybe, but not reflexively. Greedy-by-endpoint works for classic interval scheduling. But if there's a constraint like 'show segments in order' or 'maximize total duration with a limit', you'll need DP or a more complex greedy rule. Always code the DP fallback if greedy feels uncertain.
How do I even start if the problem statement is vague?+
Ask for clarification in the OA chat if available. If not, assume the simplest case: maximize the count of non-overlapping segments. Code that, then extend if test cases fail. TikTok usually rewards correct logic over guessing edge cases.
What's the common mistake candidates make?+
Ignoring the 'shown' constraint. Some assume all segments are shown and you optimize later. Others sort wrong or don't handle ties. Test your solution on a small hand-drawn example with 3-4 segments before submitting. That catches 80% of bugs.
Is this a hard problem or medium difficulty?+
Probably medium. The concept is classic, but the wording and constraints might disguise it. If you've done interval scheduling before, you'll recognize it fast. If not, DP or backtracking with memoization is the safe path.