Maximum Length of Repeated Subarray
A medium-tier problem at 51% community acceptance, tagged with Array, Binary Search, Dynamic Programming. Reported in interviews at Citadel and 0 others.
Maximum Length of Repeated Subarray shows up in live OAs, and it's the kind of problem where the brute force path leads nowhere fast. You're given two arrays and need to find the longest contiguous subarray that appears in both. Citadel has asked it. The acceptance rate sits around 51%, which means half the candidates either solve it cleanly or get stuck on the complexity. Most people think "nested loops" and time out. StealthCoder is the safety net if you freeze on the pattern during the live assessment.
Companies that ask "Maximum Length of Repeated Subarray"
Maximum Length of Repeated Subarray 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. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderThe trap is trying to compare every possible subarray of one array against the other using string matching or naive overlap. That balloons to O(n^3) or worse. The real pattern lives in Dynamic Programming or Rolling Hash. DP builds a table where each cell represents the length of matching subarrays ending at that position in both arrays, and the recurrence is straightforward once you see it. Rolling Hash lets you compare subarray hashes in constant time after O(n) preprocessing, turning the problem into a binary search on length. Most candidates who know DP solve it there. The gotcha is that the DP table is O(n*m) space and time, which is acceptable but feels slow if you don't trust the math. StealthCoder surfaces the right approach instantly if you hit the live OA and blank on whether to use DP or hash-based search.
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Maximum Length of Repeated Subarray 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. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Length of Repeated Subarray interview FAQ
Why does brute force fail on this problem?+
Brute force compares every subarray of one array against every subarray of the other, landing you at O(n^3) or O(n^2 * m) with string hashing. The assessor's test cases are sized to punish that. DP or rolling hash reduces it to O(n*m) or O(n*m*log(n)), which passes.
Is this still asked at Citadel and other quant shops?+
Yes. Citadel has a history with this problem. Quant and trading firms like array pattern-matching because it mirrors signal processing and data alignment tasks. The acceptance rate of 51% suggests it filters candidates effectively, so it stays in rotation.
Which approach is faster, DP or rolling hash?+
DP is O(n*m) time and space, simpler to code correctly under pressure. Rolling hash plus binary search is also O(n*m*log(n)) time with O(n) space but requires careful hash collision handling. DP is the safer bet in a live OA unless you're very confident in hash functions.
How does this relate to the other array topics listed?+
Binary Search pairs with rolling hash to binary-search on the length of the repeated subarray. Sliding Window doesn't directly apply here, but some variants use a two-pointer idea. Hash Function is critical if you choose the rolling hash route to avoid collisions and time limit exceeded errors.
What's the most common mistake?+
Overlooking the 2D DP state definition. Candidates write code that doesn't correctly track 'the longest match ending here' vs 'the longest match anywhere'. Also, forgetting to reset or misreading the recurrence. Spend 30 seconds on a clear state diagram before coding.
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