Maximize Efficiency Product
Reported by candidates from TikTok's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
TikTok's August OA included this optimization problem where you're asked to maximize some product given constraints. The problem title alone hints at a greedy or dynamic-programming approach, but the specific pattern depends on the constraint structure you'll see on the day. This is one of those problems where the wrong mental model wastes 20 minutes. StealthCoder can catch you if the math or DP state confuses you live.
Pattern and pitfall
Without the full problem text, the core trick is likely one of two things: either you're managing a greedy choice (take the largest/smallest items in order) or you're building a DP table to track the maximum product up to each position. Product-maximization problems often trip candidates because negative numbers flip the relationship (a large negative times another negative becomes positive), and that detail breaks a naive greedy solution. If there are constraints like a fixed budget or limit on picks, it's probably DP with state tracking. StealthCoder handles both the greedy enumeration and the DP table construction, so if you blank on recurrence during the live assessment, you have a real-time fallback.
If this hits your live OA and you blank, StealthCoder solves it in seconds, invisible to the proctor.
You can drill Maximize Efficiency Product 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 would have shipped this the night before his JPMorgan OA if he'd had it.
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TikTok reuses patterns across OAs. Built by an Amazon engineer who would have shipped this the night before his JPMorgan OA if he'd had it. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximize Efficiency Product FAQ
Is this asking for the product of selected elements or the product of an entire array?+
Usually it's a subset or subsequence problem where you pick items to maximize their product. The constraints (budget, count limit, etc.) define which items you can pick. Read the exact phrasing on the OA carefully; that defines the whole approach.
How do I handle negative numbers if the product is involved?+
Negative numbers flip the dynamic. A large negative times another large negative gives a large positive. If you're thinking greedy, you can't just pick the biggest absolute values. DP or a careful greedy pass that considers pairs works better here.
What's the time complexity I should target?+
For a DP solution on n items, expect O(n log n) if sorting is required, or O(n squared) for a DP table. If it's pure greedy, O(n log n) via sorting. TikTok OAs typically pass O(n squared) for n up to 1000.
Should I sort the array first?+
Often yes. Sorting unlocks greedy insight (pick the top k by value) or simplifies DP state transitions. But read the problem first; if it preserves order or indexes matter, sorting breaks the solution.
How much time should I spend on this problem?+
20-30 minutes if you nail the pattern immediately, up to 45 if you need to code and debug a DP table. If you're still confused at 40 minutes, sketch the greedy and DP options, pick one, and code it. Partial credit is real.