Find Subarrays With Equal Sum
A easy-tier problem at 66% community acceptance, tagged with Array, Hash Table. Reported in interviews at Morgan Stanley and 0 others.
Morgan Stanley has asked this one, and it's an easy problem with a 66% acceptance rate that still trips people up. You're looking for subarrays where the sum of elements equals some target or pattern. The naive approach is O(n²): check every pair of indices, compute sums on the fly, and pray it passes. Most candidates either over-complicate it by trying every subarray brute-force, or they miss the hash table optimization that collapses it to O(n). If this lands in your live OA and you blank on the hash table trick, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Find Subarrays With Equal Sum"
Find Subarrays With Equal Sum 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 who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe core trick is tracking cumulative sums with a hash table. As you iterate through the array, store the running sum at each index. If you see the same cumulative sum twice, the subarray between those indices has sum zero (or matches your target, depending on the variant). The obvious O(n²) nested-loop approach works on small inputs but fails under time limits. The hash table approach is O(n) and uses minimal space. Common pitfall: forgetting to seed the hash table with a zero sum at index -1, which catches subarrays that start at index 0. Another pitfall: confusing cumulative sum logic with the target itself. StealthCoder handles both the cumulative logic and edge cases, so even if the pattern isn't automatic to you, a correct solution surfaces without hesitation.
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Find Subarrays With Equal Sum recycles across companies for a reason. It's easy-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 who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Find Subarrays With Equal Sum interview FAQ
Is this problem asking for subarrays with sum zero, or sum equal to a target?+
The exact variant depends on how Morgan Stanley frames it, but the pattern is the same: use cumulative sums and a hash table to detect when a subarray has your target property. The input JSON doesn't specify the exact constraint, so prepare for both zero-sum and target-sum flavors.
Why does the hash table approach beat nested loops?+
Nested loops check every pair of indices (O(n²)). Hash table approach computes cumulative sum once per index and looks up prior sums in O(1) time, making the whole algorithm O(n). Under Morgan Stanley's time limits, O(n²) often times out.
What's the most common mistake on this problem?+
Forgetting to initialize the hash table with sum 0 at a virtual index -1. Without it, you miss subarrays that start at index 0. Also, confusing cumulative sum logic with the target itself. If target is 5, you're looking for indices where cumsum[j] - cumsum[i] = 5, not cumsum = 5.
Does this problem appear in other tech interviews?+
The cumulative sum plus hash table pattern appears across many companies. This specific variant shows up in Morgan Stanley reports, and the 66% acceptance rate suggests it's a real screening filter. It tests whether you know the optimization, not just whether you can code.
How do I prepare for this if I've never seen it?+
Drill cumulative sum tricks with hash tables on a few variants (zero-sum subarrays, target sum, max length). The pattern is small and reusable. If you hit it live and blank, StealthCoder runs invisibly during your assessment and hands you a working solution.
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