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 minu
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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 🙂
About This Question
This is a reported interview question from a cohesity interview for a swe role reported in 2025.
It covers the following topics: Binary Tree, Dynamic Programming, Graph, Greedy .
About Cohesity Interview Reports
This question was reported by a candidate who interviewed at Cohesity. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Cohesity are the higher-signal extractions to take from this report.
For broader preparation context, the Cohesity interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Cohesity reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Cohesity Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Cohesity reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.