Movie Marathon
Reported by candidates from Akuna's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
You've got a Movie Marathon question from Akuna in August, and the title alone tells you it's about optimization. No problem text means you're walking in blind, which is exactly the scenario where StealthCoder pays for itself. Movie-themed problems at trading firms almost always boil down to: pick a subset of items (movies) to maximize some value (rating, runtime, or profit) while respecting a constraint (total time or budget). This is a classic bounded knapsack or greedy selection problem. Candidates who nail it recognize the pattern fast. Those who don't waste 20 minutes chasing the wrong approach.
Pattern and pitfall
Movie Marathon is almost certainly asking you to select movies for a viewing schedule that maximizes some metric within a time window. The pattern typically splits two ways: either a dynamic programming knapsack variant (where order doesn't matter and you're picking a subset) or a greedy problem (pick movies by highest rating-per-minute until time runs out). Akuna, being a quant shop, loves constraint optimization. The trick is recognizing whether the problem asks for maximum value, maximum count, or both. Common pitfall: candidates code a greedy approach first, then realize order matters and switch to DP halfway through. If you blank on the live OA, StealthCoder will read the actual problem and surface the right pattern instantly, saving you the false start.
If you see this problem in your OA tomorrow, the play is to recognize the pattern in 30 seconds. StealthCoder buys you that recognition.
You can drill Movie Marathon 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 by an Amazon engineer who passed his OA cold and still thinks the filter is broken.
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Movie Marathon FAQ
Is this a knapsack problem?+
Probably. Movie Marathon almost certainly asks you to select movies (items) within a total time limit (capacity) to maximize some value. If movies have runtimes and ratings, it's unbounded or 0/1 knapsack. If order matters or there's a schedule constraint, it may be DP with state tracking. Read the problem first.
Could this be greedy instead?+
Yes, if the problem lets you pick movies in any order and wants you to maximize a ratio like rating divided by runtime. Greedy often works for ratio-based problems. But if you must pick a specific subset within a time window that maximizes total rating, knapsack DP is safer. The problem statement will tell you.
What's the time complexity I should aim for?+
If it's knapsack DP with N movies and a total time limit T, expect O(N*T) time and O(N*T) space. That's the standard. For greedy, you're looking at O(N log N) if you sort. Both are acceptable. Write clean DP code over trying to optimize prematurely.
How do I prepare in the next 48 hours?+
Don't panic-study random problems. Instead, drill one clean knapsack implementation (0/1, unbounded, and bounded variants). Then do one greedy selection problem. That covers 90% of 'pick items under constraint' questions. Focus on understanding the pattern, not memorizing solutions.
Do Akuna OAs usually have tricky edge cases?+
Yes. Watch for: movies with zero runtime, duplicate runtimes, edge case where no movies fit, or a time limit of zero. Also check whether you need to return the movies selected or just the max value. Read output spec carefully. That's where candidates lose points.