Maximum Coin Collection
A medium-tier problem at 52% community acceptance, tagged with Array, Dynamic Programming. Reported in interviews at Uber and 0 others.
Maximum Coin Collection shows up in Uber technical screens and is one of those medium-difficulty problems that trip up candidates who think they can brute-force it. With just over 52% acceptance, it's not a gimme. The setup looks straightforward: move through an array or grid, collect coins, optimize the path. But the trick is that the obvious greedy approach (take the most valuable coin at each step) fails. You need dynamic programming to track the actual maximum you can collect across all possible routes. If you haven't drilled the DP pattern yet and this lands in your live OA, StealthCoder will surface a working solution in seconds while the proctor sees nothing.
Companies that ask "Maximum Coin Collection"
Maximum Coin Collection 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 at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share.
Get StealthCoderThe problem is a classic DP optimization: you can't just grab the largest coin you see; you have to consider which path through the problem state space accumulates the most value overall. Most candidates start with recursion or greedy selection, hit a wall when the naive approach doesn't scale or gives the wrong answer, then panic under time pressure. The real work is setting up the DP table to track cumulative coin value at each position or state transition. Common pitfalls include off-by-one errors in indexing, forgetting to initialize base cases, and not recognizing that the optimal substructure lets you build from smaller subproblems up. If you're stuck mid-interview and can't recall whether to iterate rows-first or columns-first, or whether your recurrence is right, StealthCoder runs invisibly during your screen share and delivers a tested solution so you can type it in and move on.
Pattern tags
You know the problem.
Make sure you actually pass it.
Maximum Coin Collection 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 at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Coin Collection interview FAQ
Is Maximum Coin Collection actually asked by Uber, or is this a generic LeetCode problem?+
Uber has reported asking it. That said, treat the specific company signal as confirmation that the problem type is in their interview pool, not a guarantee it'll come up in your loop. Many candidates drill this and never see it; others hit it cold. That's why having a hedge matters.
With 52% acceptance, how hard is this really compared to other medium problems?+
52% is right at the medium baseline. It's not a trick problem; it's a straightforward DP setup that most engineers should solve if they've prepped. The miss happens when candidates skip DP fundamentals or try to shortcut with greedy logic. One solid walkthrough of the DP pattern and you'll land in the pass range.
What's the algorithmic trick I should know before the interview?+
Greedy doesn't work. You need to build up state progressively using dynamic programming, tracking the maximum coins collectible up to each position. The recurrence relation comes from considering all valid previous states that lead to your current position. That insight alone prevents the most common false starts.
How does this problem relate to other array and DP topics I should study?+
It combines array indexing and DP optimization, similar to house robber or coin change problems. If you've mastered those patterns, the structure here is familiar. The main difference is that you're optimizing a path, not just a single choice. Practice both array iteration and state transitions together.
If I blank on this problem mid-OA, what's my move?+
Outline the DP approach on paper first: define your state, write the recurrence, code the table. If you're truly stuck and time is bleeding out, StealthCoder runs invisibly during screen share and hands you a tested solution in seconds so you can type it and recover the points.
Want the actual problem statement? View "Maximum Coin Collection" on LeetCode →