Practice OpenAI Coding Question | Real Coding Interviews

Quick Summary

Schedule data labeling assignments by pairing tasks, models, and human annotators.
Ensure each human completes at least k distinct tasks without repeating any task.
Return any valid assignment list or an empty list if constraints make it impossible.

What This Tests

Feasibility checking and constraint validation
Greedy construction and systematic assignment generation
Cyclic indexing and modular arithmetic
Output formatting and list manipulation

Common Patterns

Greedy

Simulation

Modular Arithmetic

A data labeling platform manages tasks, human annotators, and AI models. Each entity is represented by a 1-indexed string ID.

You are given integers totalTask, totalModel, totalHuman, and k:

Tasks are labeled "t1", "t2", ..., "t{totalTask}".
Models are labeled "m1", "m2", ..., "m{totalModel}".
Human annotators are labeled "h1", "h2", ..., "h{totalHuman}".

An assignment is represented as [task, model, human], meaning the specified human annotates the specified task using the specified model. A task may appear in multiple assignments, meaning the same task can be annotated by multiple humans and paired with multiple models.

Return any valid schedule as a list of assignments such that:

Stdout