Jump Game VI
A medium-tier problem at 46% community acceptance, tagged with Array, Dynamic Programming, Queue. Reported in interviews at AQR Capital Management and 0 others.
Jump Game VI is a medium-difficulty DP problem that looks deceptively simple: you're given an array of integers and need to reach the last index while maximizing your score. Each element tells you how far you can jump; each cell adds to your score. The trap is that a greedy approach fails. You need dynamic programming to track the best possible score at each position. It's been asked at AQR Capital Management and sits at around 46% acceptance, which means most candidates either miss the DP pattern or implement it inefficiently. If this problem hits your live assessment and you blank on the optimal state transition, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
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Jump Game VI 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 Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code.
Get StealthCoderThe naive greedy strategy (always jump as far as possible) doesn't maximize score. Instead, you must compute the maximum score reachable at each index using DP: dp[i] equals the maximum score at position i given the jump constraints. The challenge isn't the recurrence relation itself, but recognizing that naive DP is O(n^2) and becomes too slow on large arrays. The key optimization is using a monotonic queue or heap to track the maximum dp value within the jump range efficiently, dropping it to O(n). Most candidates implement the basic DP and don't think about the queue optimization, causing TLE on hidden test cases. Understanding when to apply monotonic queue for range maximum queries separates accepted solutions from timed-out ones. StealthCoder catches this if you implement the slower version live and panic.
Pattern tags
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Jump Game VI 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 Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Jump Game VI interview FAQ
How is Jump Game VI different from Jump Game I?+
Game I is a reachability problem (can you reach the end). Game VI asks you to maximize your score along the way. Reachability is solvable greedily or with simple DP. Maximization requires tracking the best score at each position and recognizing that range-max queries need optimization, not just a recurrence.
Why doesn't greedy work on this problem?+
Greedy jumps as far as possible each step, but a far jump might land on a low-value cell, blocking better paths. DP explores all reachable positions and keeps track of the maximum score, not just the distance. The optimal path sometimes takes smaller jumps to land on high-value intermediate cells.
Is the monotonic queue optimization required to pass?+
Depends on the test constraints. If n is under 1000, O(n^2) DP usually passes. Larger arrays (n > 10,000) almost always require monotonic queue or heap to avoid TLE. Most online assessments include both constraint ranges, so learning the optimized version is safer.
What's the relationship between this problem and the topics listed?+
Array and DP are the core algorithm. Monotonic Queue and Heap (Priority Queue) are both valid optimizations to reduce the range-max lookup from O(n) to O(log n) or amortized O(1). Queue is more elegant for this specific structure; many solutions use a deque to maintain decreasing order.
Is this problem still asked at top companies?+
It's been reported at AQR Capital Management. With a 46% acceptance rate, it's not uncommon in technical screens, especially for roles valuing optimization skills. It's a solid signal of whether you understand DP trade-offs and when to apply advanced data structures.
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