Maximum Points After Enemy Battles
A medium-tier problem at 33% community acceptance, tagged with Array, Greedy. Reported in interviews at Rubrik and 0 others.
Maximum Points After Enemy Battles is a medium-difficulty greedy problem that appears in live coding assessments, most recently at Rubrik. The 32% acceptance rate signals a gap between the obvious greedy approach and the one that actually works. Most candidates intuition-fail on the strategy for which enemy to fight first, then waste ten minutes rewriting. If this problem hits your live OA and you freeze on the greedy choice, StealthCoder surfaces the solution invisible to the proctor. The trick isn't complex once you see it, but the assessment environment is where that gap costs you.
Companies that ask "Maximum Points After Enemy Battles"
Maximum Points After Enemy Battles 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. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe problem asks you to maximize points by choosing which enemies to battle in sequence, with each victory letting you attack again. The trap is assuming you should always fight the weakest enemy first. Actually, you want to fight the strongest enemy whenever your current points exceed their power, because that greedy choice is irreversible and cash-efficient. The key insight: if you can't beat the strongest, you're stuck battling the weakest repeatedly until you accumulate enough points. Most candidates sketch the wrong order, realize it midway through testing, then thrash through a rewrite. Array iteration and conditional logic are straightforward. The greedy decision itself is where most interviews see stalls. StealthCoder spots the pattern instantly and gives you working code so you can move on.
Pattern tags
You know the problem.
Make sure you actually pass it.
Maximum Points After Enemy Battles 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. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Points After Enemy Battles interview FAQ
Is this a FAANG problem or just Rubrik?+
It's reported from Rubrik. Lower-frequency problem in the broader interview circuit, but that doesn't make it easier. Companies using similar assessment platforms will reuse or adapt it. If greedy arrays are on your prep list, this pattern matters.
What's the core trick I need to see?+
Fight the strongest enemy whenever your points exceed their power. Once you beat them, you can keep attacking. If you can't beat the strongest yet, grind the weakest. The greedy choice is to prioritize irreversible wins, not to minimize each battle.
How does this relate to typical greedy problems?+
Standard greedy problems ask 'what's the locally optimal choice at each step.' This one adds a constraint: some choices are blocked until you meet a threshold. That's where most candidates slip. You're not just picking the best action, you're understanding when actions become available.
Will I have time to code this during the OA?+
If you see the greedy pattern, yes. The code itself is a loop with conditionals and array operations, maybe 10 to 15 lines. The risk is spending eight minutes on the wrong strategy. That's what the 32% acceptance tells you.
Should I study greedy and array topics separately before this problem?+
Yes. Understand the greedy method conceptually and practice array iteration. Then see how this problem combines them under a constraint. Skipping greedy fundamentals and jumping here will cost you more time than it saves.
Want the actual problem statement? View "Maximum Points After Enemy Battles" on LeetCode →