Quick Overview

This coding question tests practical implementation of top-p (nucleus) sampling, a core decoding strategy in large language models. It evaluates NumPy proficiency including numerically stable softmax, cumulative probability thresholding, and reproducible stochastic sampling — skills central to machine learning engineering roles.

Implement Top-p (Nucleus) Sampling in NumPy

Company: Amazon

Role: Machine Learning Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Onsite

Quick Answer: This coding question tests practical implementation of top-p (nucleus) sampling, a core decoding strategy in large language models. It evaluates NumPy proficiency including numerically stable softmax, cumulative probability thresholding, and reproducible stochastic sampling — skills central to machine learning engineering roles.

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