Nutanix Online Assessment for Python Automation Testing (OA Review)
Question Details
Problem Statement Data Engineers must schedule n long-running tasks on remote servers while minimizing total cost. There are two available servers: 1.
Paid Server: Processing task i costs
Full Details
Problem Statement Data Engineers must schedule n long-running tasks on remote servers while minimizing total cost. There are two available servers: 1.
Paid Server: Processing task i costs cost[i] and takes time[i] units of time. 2.
Free Server: Processing any task costs 0 and takes 1 unit of time. However, the free server can only process tasks while the paid server is occupied. Since the free server processes tasks at a rate of 1 per time unit, running task i on the paid server allows time[i] other tasks to be processed on the free server simultaneously.
Example
Input: n = 4, cost = [1, 1, 3, 4], time = [3, 1, 2, 3] *
Strategy: Schedule the first task (index 0) on the paid server. *
Cost: 1. *
Time on Paid: 3 units. *
Effect: During these 3 units, the remaining 3 tasks are processed on the free server (taking 1 unit each). *
Result: All 4 tasks are completed for a total cost of 1.
Function Description * cost[n]: Integer array representing the cost of each task on the paid server. * time[n]: Integer array representing the duration of each task on the paid server.
Constraints * $1 \le n \le 10^3$ * $1 \le cost[i] \le 10^6$ * $1 \le time[i] \le 10^3$
Solution Approach This problem can be modeled as a variation of the 0/1 Knapsack Problem. The objective is to select a subset of tasks to run on the paid server such that the remaining tasks can be covered by the free server. * If task i is chosen for the paid server, it contributes time[i] units of "free server capacity." * Additionally, task i itself is completed, effectively covering 1 task count. * Therefore, running task i on the paid server contributes a total "value" of time[i] + 1 towards the total number of tasks n. The problem reduces to finding the minimum cost to achieve a total combined time value of at least n.
Algorithm: 1. Initialize a DP array dp of size n + 1 with infinity, where dp[0] = 0. 2. dp[j] represents the minimum cost to cover j tasks. 3. Iterate through every task i with associated c = cost[i] and t = time[i]. 4. Update the DP table in reverse (from n down to 1): * dp[j] = min(dp[j], dp[max(0, j - t - 1)] + c) 5. The answer is stored in dp[n].
Time Complexity: $O(n^2)$
Space Complexity: $O(n)$
About This Question
This is a reported interview question from a nutanix interview for a swe role during the oa round reported in 2025.
It covers the following topics: Arrays, Dynamic Programming, Sql .
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This question was reported by a candidate who interviewed at Nutanix. 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 Nutanix are the higher-signal extractions to take from this report.
For broader preparation context, the Nutanix 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 Nutanix reports consistently are the ones worth investing in; one-off niche problems are not.
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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 Nutanix 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.