Max Reward (Intuit India)
Reported by candidates from Intuit's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
You're facing Intuit's Max Reward problem, reported in February 2024. This is a classic greedy or dynamic-programming trap that tests whether you optimize locally or think ahead. Candidates often grab the highest immediate reward and tank on test cases where patience pays off. You have a couple of days to nail the pattern. StealthCoder is your safety net if you blank during the live assessment and need the core logic fed back to you in real time.
Pattern and pitfall
Max Reward problems typically hinge on understanding whether you should pick the best option now or defer for a better combo later. The greedy approach works when you can prove a locally optimal choice never blocks a globally optimal one. More often, you need dynamic programming: state is your current position or reward count, and you explore both paths (take now, or skip and come back). The trick isn't the code, it's recognizing that sorting by reward alone misleads you. You need to factor in constraints: are there limits on picks, time windows, or dependencies between rewards? Common pitfall: jumping to greedy without proving it works. During the OA, if you blank on the DP formulation, StealthCoder reads the problem and suggests the state space and transition logic so you can code it out.
The honest play: practice the pattern, and have StealthCoder ready for the one you didn't see coming.
You can drill Max Reward (Intuit India) 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 for the candidate who saw this exact problem leak two days before his OA and wondered if anyone had a play.
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Max Reward (Intuit India) FAQ
Is this problem asking for the maximum sum or the maximum count of rewards?+
Without the full problem text, it's impossible to say for certain. But "max reward" usually means maximum total value, not count. Check the examples in your OA carefully. If you're unsure, ask yourself: does order matter, and are there penalties for skipping?
Should I use greedy or DP?+
Greedy only works if you can prove a local choice never regrets. Sort by reward, pick the largest, and trace through an example where that fails. If it does, DP is safer. Use state like dp[i] = max reward up to choice i, then try taking or skipping each reward.
How do I prepare for this in 48 hours?+
Practice two things: one classic greedy problem (activity selection, coin change with coins), and one interval DP problem (house robber, word break). That covers both poles. Then read the Intuit problem three times, sketch the state machine, and code it.
What's the most common wrong answer Intuit candidates submit?+
Sorting by reward descending and greedily picking without checking constraints or dependencies. Test your greedy solution against a case where two mid-sized rewards beat one large one. If it fails, you need DP.
Does Intuit ask follow-ups like optimizing space or time?+
Possibly, especially if your first solution is O(n^2). Have a memoized recursive version ready, and know how to optimize space by using a rolling array or two pointers if the DP is 1D.