Maximize Happiness
Reported by candidates from Rubrik's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Rubrik's August OA included a happiness maximization problem that looks like a greedy or dynamic programming play at first glance. You're handed some constraint (likely an array or resource count) and need to find the configuration that maximizes a happiness score. The trick is figuring out whether you can just sort and pick greedily, or if you need to track state across multiple choices. StealthCoder will surface the pattern in real time if you freeze mid-problem.
Pattern and pitfall
This problem almost always hinges on recognizing whether happiness is additive and monotonic. If you can model it as picking items to maximize a sum subject to a constraint, greedy sorting by efficiency (happiness per unit cost) often works. If happiness depends on the *order* or *combination* of picks, you'll need dynamic programming to avoid leaving value on the table. The common trap is assuming greedy works when the problem has hidden dependencies. Watch for whether picking item A affects the benefit of picking item B. If it does, DP is your safety net. If it doesn't, sort and accumulate. StealthCoder catches the moment you commit to the wrong approach and can redirect before you code.
Drill it cold or hedge it with StealthCoder. Either way, don't walk into the OA hoping you remember the trick.
You can drill Maximize Happiness 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. Made for the candidate who got the OA invite this morning and has 72 hours, not six months.
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Maximize Happiness FAQ
Is this a greedy or DP problem?+
If happiness is independent (picking A doesn't change B's value), greedy works. Sort by value-to-cost ratio, pick in order. If happiness depends on combinations or order, it's DP. Test on small examples: can you always pick the local best without regretting it later.
What's the most common mistake on happiness problems?+
Assuming greedy works when the problem has coupled constraints. For example, if picking one item reduces capacity in a weird way, or happiness compounds only when items are picked in sequence, greedy fails. Code a DP solution if you're unsure.
How do I prepare for this in 24 hours?+
Solve two problems: one pure greedy (activity selection or interval scheduling) and one DP (unbounded knapsack or coin change). That covers 90 percent of happiness OA variants. Practice recognizing the dependency structure, not just coding.
Should I sort by happiness or by cost first?+
Sort by happiness-to-cost ratio if the constraint is a resource limit. Sort by happiness alone if you can pick freely up to a count limit. Read the problem statement twice to confirm the bottleneck.
Is this still asked at Rubrik?+
Yes. Happiness maximization shows up often in resource allocation and optimization rounds. It tests whether you can model a real business problem and pick the right algorithmic tool. Expect it if you're in a systems or backend track.