Task Scheduling with Block Partitioning | Dataford Interview Questions

Given task process times and numServer servers, each server takes a contiguous block of at least m tasks and its processing time is the sum of its m largest tasks; find the maximum possible total processing time.

Asked in the online assessment stage.

Implement maximize_processing_time(task_times, numServer, m). The input is an integer array, a positive server count, and a positive integer m. Return the maximum integer total processing time after partitioning all tasks into exactly numServer contiguous blocks, each containing at least m tasks.

Constraints

2 <= len(task_times) <= 300
1 <= m <= len(task_times)
1 <= numServer <= len(task_times) // m
0 <= task_times[i] <= 10^9
All tasks must belong to exactly one contiguous server block

Function Signature

def maximize_processing_time(task_times, numServer, m):

Your question is Task Scheduling with Block Partitioning. Start with the requirements in the Question tab.

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Problem

Asked in the online assessment stage.

Constraints

2 <= len(task_times) <= 300
1 <= m <= len(task_times)
1 <= numServer <= len(task_times) // m
0 <= task_times[i] <= 10^9
All tasks must belong to exactly one contiguous server block

Function Signature

def maximize_processing_time(task_times, numServer, m):

Your question is Task Scheduling with Block Partitioning. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.