Cohesity Interview Questions (2026)
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Cohesity Interview: Binary Tree DP Problem (Maximum Non-Adjacent Subset Sum)
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Cohesity Interview Experience – Binary Tree DP (Learning Moment) Recently, I interviewed with Cohesity and wanted to share one learning experience from the round. The interview started with a ~10 minute discussion about my current work. We talked about my day-to-day responsibilities, the systems I work on, and the kind of problems I usually solve. The discussion was smooth and conversational. After that, the interviewer moved to a coding problem. ❓ Problem Asked Given a binary tree, find the maximum subset sum such that no two selected nodes are adjacent (i.e., you cannot pick both a parent and its child). This is essentially the “Maximum Sum of Non-Adjacent Nodes in a Binary Tree” problem (similar to House Robber III on LeetCode). 😅 What Happened Although I understood the problem constraints, I struggled to formulate the correct dynamic programming approach on a tree during the interview. I tried thinking in terms of traversal and greedy choices, but I couldn’t arrive at the clean solution within the time. In hindsight, the key was realizing that this is a tree DP problem, where for each node you need to track two states: when the node is included when the node is excluded ✅ Correct Approach (What I Learned) For each node: Include node → cannot include its children Exclude node → children can be included or excluded independently Return a pair {take, skip} for each node: take = node->val + left.skip + right.skip skip = max(left.take, left.skip) + max(right.take, right.skip) Final answer: max(root.take, root.skip) This solution runs in O(n) time and is very elegant once you see it. 📌 Takeaway This interview was a great reminder that: Tree problems often require DP with multiple states If a constraint says “cannot pick adjacent nodes”, think in terms of include / exclude Practicing classic patterns (Tree DP, DFS with return states) really matters Although I couldn’t solve it during the interview, it was a valuable learning experience and highlighted areas I’ve since focused on improving. Sharing this here so it helps others who might face a similar question 🙂
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Cohesity Interview Process Overview
The Cohesity interview process typically includes a recruiter screen, one to two technical phone screens, and a 4-6 round on-site or virtual on-site loop. Each round serves a distinct calibration purpose: coding rounds measure correctness, code quality, and complexity reasoning; system design rounds measure architectural judgment at the appropriate level; behavioral rounds measure ownership, leadership scope, and collaboration. Reports tagged on LeakCode from 2024-2026 show Cohesity runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Cohesity coding rounds typically run medium difficulty with follow-up depth as the senior discriminator. System design rounds expect production-grade trade-off articulation at L4+ levels. Behavioral rounds expect quantified outcomes ("reduced p99 latency from 800ms to 120ms") rather than vague impact claims. The candidates who advance consistently demonstrate clear thinking out loud rather than perfect final answers.
How To Use Cohesity Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Cohesity updates its question pool every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage approach: identify the patterns that appear repeatedly in Cohesity reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions above by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Cohesity's pool. Reports tagged with quantified difficulty and explicit round type are higher-signal than reports without those tags. The metadata filters help you build a focused study plan in 1-2 hours rather than 8-10 hours of unstructured browsing.
Common Cohesity Interview Mistakes
Reports tagged "no hire" at Cohesity consistently surface a few patterns: jumping into code without clarifying requirements, coding silently for extended periods, missing edge cases (empty input, single element, large input, overflow), producing working code the candidate cannot refactor when probed, and behavioral stories that use "we" instead of "I" diluting individual signal. Strong candidates explicitly avoid these patterns by following a consistent round template.
The single most predictive failure mode in recent reports: not asking clarifying questions. Interviewers are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into implementation immediately. Strong candidates also verbalize their approach before writing code; weak candidates code in silence and lose the communication dimension of the round's calibration.