Maximum Points in an Archery Competition
A medium-tier problem at 50% community acceptance, tagged with Array, Backtracking, Bit Manipulation. Reported in interviews at Kakao and 0 others.
Maximum Points in an Archery Competition is a medium-difficulty problem that appears in Kakao assessments. You're distributing arrows across ten targets to maximize your score while your opponent does the same. The trick isn't greedy. Most candidates first try to brute-force or assume some optimal ordering exists, then hit a wall when they realize the state space is larger than it looks. If this lands in your live OA and you blank on how to enumerate all valid distributions, StealthCoder surfaces a working solution invisible to the proctor. The problem forces you to think in terms of state exploration, not pure math.
Companies that ask "Maximum Points in an Archery Competition"
Maximum Points in an Archery Competition 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 StealthCoderThis is a backtracking and enumeration problem dressed up as optimization. You have a fixed budget of arrows to distribute across ten targets. Your opponent has their own fixed distribution. For each target, you either beat them by one arrow (to secure those points) or don't contest it at all. The state space is all possible ways to allocate your arrows. Brute force enumeration with pruning works, but many candidates try greedy first (e.g., always contest the highest-value targets) and fail. Bit manipulation can encode which targets you contest, and backtracking explores distributions systematically. Common pitfall: not recognizing that you don't need to enumerate every single arrow count per target, only whether you contest each target and by how much. Once you map the problem as exploring a tree of allocation decisions, the pattern becomes clear. This is exactly the kind of problem where the obvious path fails and a hidden structural insight matters.
Pattern tags
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Make sure you actually pass it.
Maximum Points in an Archery Competition recycles across companies for a reason. It's medium-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.
Maximum Points in an Archery Competition interview FAQ
Is this a greedy problem or full enumeration?+
Full enumeration with pruning. Greedy fails because you can't know which targets to contest without exploring trade-offs. You must explore the space of allocation decisions using backtracking to find the maximum score.
How do I handle the arrow budget constraint?+
Track remaining arrows in your recursion. At each target, decide how many arrows to use (0 to beat opponent, or opponent+1 to win). Stop when you run out of arrows or reach the last target. This naturally enforces the constraint.
When was this last asked at Kakao?+
It's in the Kakao problem record. Kakao favors enumeration and backtracking problems, so if you're interviewing there, expect similar constraint-exploration patterns.
What role does bit manipulation play?+
You can represent which targets you contest using a bitmask. However, bit manipulation is optional here; backtracking alone solves it. Use bits only if you want to cache states compactly or iterate over all subsets.
How do I avoid timing out?+
Prune aggressively. Stop exploring a branch if remaining arrows can't improve the best score found. Also, once you contest a target, you know the minimum arrows needed, so skip unnecessary iterations over smaller counts.
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