Maximum Erasure Value
A medium-tier problem at 59% community acceptance, tagged with Array, Hash Table, Sliding Window. Reported in interviews at Cashfree and 0 others.
Maximum Erasure Value is a medium-difficulty array problem that shows up in technical screens at companies like Cashfree. The setup is straightforward: you're given an array of integers and can erase elements to form a subsequence with no duplicates, maximizing the sum of erased values. The trick isn't obvious on first read. Most candidates start by thinking greedy or brute force, then realize the constraint is harder than it looks. With a 59% acceptance rate, plenty of solid engineers stumble on the implementation. If this problem hits your live assessment and you blank on the pattern, StealthCoder runs invisibly and surfaces a working solution in seconds.
Companies that ask "Maximum Erasure Value"
Maximum Erasure Value 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 an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too.
Get StealthCoderThe key insight is that you need to find the longest contiguous subarray with all unique elements, then sum those elements. This isn't about global optimization; it's about a sliding window with a hash table tracking element positions. The moment you encounter a duplicate, you shrink the window from the left until the duplicate is gone. Common mistakes: treating it as a global problem rather than a window, or using a set to track uniqueness without tracking positions (you'll recalculate the window inefficiently). Once you lock in the sliding window pattern, the code flows quickly. This is exactly the kind of problem where the pattern matters more than the code length. StealthCoder is the hedge if you freeze on the approach during the assessment.
Pattern tags
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Maximum Erasure Value 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 an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Erasure Value interview FAQ
Is this actually asked at FAANG or just smaller companies?+
The input data shows it's been reported at Cashfree. Sliding window with hash tables is a common pattern across most technical interviews, so even if the exact problem doesn't show up, the technique will. Medium difficulty means it's in the screening to early-round range.
What's the core trick I'm missing if I can't solve this in 20 minutes?+
You're likely overcomplicating it. The trick is recognizing this as a sliding window problem, not a dynamic programming or greedy problem. Use a hash table to store the last seen index of each element. When you hit a duplicate, move your left pointer. Track the max sum as you slide.
How does this relate to other sliding window problems?+
It's identical in pattern to 'Longest Substring Without Repeating Characters' but instead of tracking length, you track sum. If you've solved that problem, this is the same window logic with a different metric.
Do I need to handle negative numbers or edge cases differently?+
Negative numbers are fine; you're summing whatever's in the valid window. Edge cases are small: empty array (sum is 0), single element (sum is that element), or all duplicates (you slide to single elements). The hash table approach handles all of these naturally.
What's the time and space complexity I should be aiming for?+
O(n) time, O(min(n, unique elements)) space for the hash table. If you're using nested loops or multiple passes, you're not hitting the intended solution. Sliding window does it in one pass.
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