Cohesity coding interview
questions, leaked.
3 problems reported across recent Cohesity interviews. Top patterns: design, heap priority queue, hash table. The list below is what most reported candidates actually saw, plus the honest play if you can't grind all of it.
Cohesity's interview is tight. Three problems on record, and they're not padding the list with easy warm-ups. You're facing zero easy questions, two mediums, and one hard. Design shows up twice, heap and hash-table operations are critical, and the problems themselves are classics: LRU Cache, Path With Minimum Effort, Find Median from Data Stream. These aren't trick questions. They're testing whether you can code under pressure. If you blank mid-assessment, StealthCoder runs invisibly and surfaces a working solution in seconds.
Top problems at Cohesity
| # | Problem | Diff | Frequency | Pass % | Patterns |
|---|---|---|---|---|---|
| 01 | LRU Cache | MEDIUM | 100.0 | 45% | Hash Table · Linked List · Design |
| 02 | Path With Minimum Effort | MEDIUM | 88.3 | 61% | Array · Binary Search · Depth-First Search |
| 03 | Find Median from Data Stream | HARD | 88.3 | 53% | Two Pointers · Design · Sorting |
Frequencies derived from public community-tagged interview reports. Click a row to view on LeetCode.
You have a week, maybe less. You can't out-grind the list above. StealthCoder runs invisibly during the actual Cohesity OA. The proctor cannot see it. Screen share cannot detect it. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoder- design2 · 67%
- heap priority queue2 · 67%
- hash table1 · 33%
- linked list1 · 33%
- doubly linked list1 · 33%
- array1 · 33%
- binary search1 · 33%
- depth first search1 · 33%
- breadth first search1 · 33%
- union find1 · 33%
Design dominates here. LRU Cache alone forces you to marry hash-table O(1) lookup with doubly-linked-list O(1) eviction. Find Median from Data Stream is the same game: two heaps, real-time state management. Path With Minimum Effort hits breadth-first-search and union-find, but it's the one problem where you could ship a working brute force and move on. The hard question is the outlier but not the shock. Most candidates will spend prep time on LRU Cache because it's a standard interview. Path With Minimum Effort is where people get tripped up on graph traversal. On the live assessment, if you hit either and freeze, StealthCoder is your hedge. You drill the two design problems now, and trust the framework for the traversal edge cases.
Companies with similar patterns
If you prepped for Cohesity, these companies recycle ~60% of the same topics.
You've seen the list.
Now make sure you pass Cohesity.
Memorizing every problem above in a week is a fantasy. StealthCoder is the hedge: an AI overlay that's invisible during screen share. It reads the problem on screen and surfaces a working solution in under 2 seconds. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Cohesity interview FAQ
Should I focus on design patterns or algorithms first for Cohesity?+
Design first. It appears twice in their top problems and combines multiple data structures. LRU Cache and Find Median from Data Stream both require careful state management, not just algorithm knowledge. Only after nailing design should you drill graph traversal.
How many heap problems should I solve before the OA?+
Heap appears in two of their three top problems, but not as the primary focus. Solve enough to feel comfortable implementing a min and max heap from scratch, then shift to the design patterns those heaps support. Quality over reps here.
Is binary search worth drilling for Cohesity?+
It appears in Path With Minimum Effort, but that problem is more about graph search than binary search itself. Understand binary search on answer space, but don't spend a full day on it. The other two problems are higher ROI.
What's the risk if I skip hash-table prep?+
LRU Cache is built on hash-table O(1) access. You can't solve it without a solid hash-table mental model. Even if you get the linked-list part right, weak hash-table intuition will slow you down. Don't skip it.
How many problems do I need to solve to be ready?+
Three problems are on record. You don't need to grind fifty variations. Solve LRU Cache three times until the code flows. Solve Find Median from Data Stream twice. Solve Path With Minimum Effort once. Focus beats volume.