Maximum Number of Ones
A hard-tier problem at 69% community acceptance, tagged with Math, Greedy, Sorting. Reported in interviews at Qualcomm and 0 others.
Maximum Number of Ones is a hard-rated problem that looks deceptively simple on the surface but trips up candidates who jump to the obvious greedy move. Qualcomm has asked this, and acceptance runs around 69%, which means the majority of people who attempt it walk away with nothing. The trick isn't about brute force or exhaustive search. It's about recognizing when a greedy choice actually works, and why the order you process things matters. If you hit this in your assessment and your first instinct starts failing, StealthCoder solves it in seconds without the proctor seeing a thing.
Companies that ask "Maximum Number of Ones"
Maximum Number of Ones 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 by a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE.
Get StealthCoderThe problem forces you to think about trade-offs. You're maximizing something (ones, typically in a binary representation or similar constraint), but the naive approach of 'just pick the biggest first' doesn't cut it. Math, Sorting, and Greedy are all in play, which means you need to understand why certain orderings unlock the answer. The heap topic hints that priority matters. Common fail: candidates sort by one metric, realize it doesn't work, then panic and try random permutations. The real pattern usually involves sorting by a different dimension (cost per unit, efficiency, reverse ratio) and then greedily picking until constraints are exhausted. Understanding the mathematical structure of the constraint is what separates the pass from the timeout or wrong answer. During a live OA, if you've drilled this pattern, you'll spot it in 3 minutes. If you haven't, StealthCoder is your invisible safety net.
Pattern tags
You know the problem.
Make sure you actually pass it.
Maximum Number of Ones 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 by a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Number of Ones interview FAQ
Is this problem actually about binary ones, or is 'ones' metaphorical?+
The problem likely uses 'ones' as a concrete concept you need to maximize under constraints, not metaphorical. Read the problem statement carefully. The trick is recognizing what dimension to sort or prioritize by. The acceptance rate (69%) suggests the pattern isn't immediately obvious from the title alone.
Why does greedy work here when it fails on similar problems?+
Greedy works because of a specific mathematical property of the constraint. You have to prove that picking the locally optimal choice (in the right order) never makes a later choice worse. The trick is identifying the correct sort key. Most failures happen from sorting by the wrong metric and hitting wrong answer on test cases.
Should I use a heap or just sort?+
Depends on the constraint structure. If you're repeatedly picking max from a shrinking set, heap is natural. If you process everything in one pass after sorting, plain sort is cleaner and faster. The topic list includes both, suggesting the final solution is likely one or the other, not both.
How does this relate to the Math and Greedy topics?+
Math tells you the structure and why greedy works. Greedy is the algorithm. You sort or prioritize mathematically (cost-to-benefit ratio, for example), then greedily consume. The math proves greedy is optimal; greedy is the implementation.
What's the most common mistake candidates make?+
Sorting by the wrong metric. Candidates pick a dimension that feels right but doesn't actually optimize the outcome. They test on the example, it works, then they fail hidden cases. Double-check your sort key makes intuitive sense for why you're maximizing ones, not just size or count.
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