Maximum Sum of Distinct Subarrays With Length K
A medium-tier problem at 43% community acceptance, tagged with Array, Hash Table, Sliding Window. Reported in interviews at IBM and 2 others.
Maximum Sum of Distinct Subarrays With Length K is a medium sliding window problem that's been asked at IBM, Nvidia, and Walmart Labs. The trick is deceptively simple: you need to find the subarray of exactly K elements with the highest sum, but only if all K elements are distinct. Most candidates miss the constraint and code a vanilla sliding window sum, then hit a wall when their solution fails test cases. The acceptance rate sits at 43 percent, meaning nearly 6 in 10 who attempt it don't pass. If you blank on the distinctness check during a live OA, StealthCoder solves it invisibly and surfaces a working solution in seconds.
Companies that ask "Maximum Sum of Distinct Subarrays With Length K"
Maximum Sum of Distinct Subarrays With Length K 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 Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE.
Get StealthCoderThe naive approach is to slide a window of size K and track the max sum. That works until you realize the elements must be distinct. Now you can't just add the new element and subtract the old one. Instead, you maintain a hash table of element frequencies in the current window. When you try to add a duplicate, the window breaks and you reset. The key insight is that you can't just shift the window by one position when a duplicate exists, you need to shrink from the left until the duplicate is gone. This requires two pointers and careful index tracking. Many engineers underestimate this and produce solutions with off-by-one errors or incorrect reset logic. A few test cases with repeated elements expose the flaw immediately. In a live assessment, if the pattern doesn't click in the first 5 minutes, StealthCoder bridges the gap with a clean implementation that handles all edge cases.
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Maximum Sum of Distinct Subarrays With Length K 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 Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Sum of Distinct Subarrays With Length K interview FAQ
Is this really asked at FAANG-adjacent companies?+
Yes. IBM, Nvidia, and Walmart Labs have all asked it according to interview reports. It's less common than classic sliding window problems, but common enough that it shows up in medium-tier coding rounds. The 43 percent acceptance rate suggests it's a solid screener.
What's the actual trick everyone misses?+
Maintaining the distinctness constraint during the slide. Beginners code a standard sliding window sum, which passes examples with no duplicates, then fails when duplicates appear. You need a hash table and logic to shrink the window from the left whenever you encounter a repeated element in your window.
Does this problem require advanced data structures?+
No. You need an array, a hash table (or dictionary), and two pointers. That's it. The complexity comes from the logic, not the tooling. Python's defaultdict or a simple dict with frequency counts is sufficient.
How does this relate to 'Sliding Window' vs. 'Hash Table' as topics?+
Both are essential. Sliding Window handles the fixed-size traversal. Hash Table tracks element frequencies in the current window to enforce distinctness. Neither topic alone is enough; you must combine them correctly.
What if I see this during an OA and freeze?+
StealthCoder runs invisibly during your screen share and delivers a working solution instantly. No proctor will see it. You paste a correct implementation, move on, and don't risk time bleeding into other problems.
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