Task Scheduling Approach | Dataford Interview Questions

Describe a time you designed or implemented task scheduling for a data or ML workflow.

For the coding exercise, implement schedule_tasks(tasks, workers) to produce executable batches while respecting task dependencies and the worker limit. Each task is a dictionary with a unique string id and a list of dependency IDs. Return a list of batches, where each batch contains task IDs that may run concurrently. Return [] if the dependency graph contains a cycle.

Input/output: tasks is a list of dictionaries and workers is a positive integer; return list[list[str]].

Constraints

0 <= tasks.length <= 10^4
1 <= workers <= 10^4
Each task ID is unique
Every dependency ID refers to a task in tasks
The dependency graph may contain cycles
Each task dictionary contains id and dependencies

Function Signature

def schedule_tasks(tasks, workers):

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Problem

Describe a time you designed or implemented task scheduling for a data or ML workflow.

Input/output: tasks is a list of dictionaries and workers is a positive integer; return list[list[str]].

Constraints

0 <= tasks.length <= 10^4
1 <= workers <= 10^4
Each task ID is unique
Every dependency ID refers to a task in tasks
The dependency graph may contain cycles
Each task dictionary contains id and dependencies

Function Signature

def schedule_tasks(tasks, workers):

Your question is Task Scheduling Approach. 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.