How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Onsite rounds at Amazon.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Amazon during technical interviews.

Build a package-allocation model for couriers

Quick Overview

This question evaluates a candidate's ability to design an end-to-end ML-driven package-to-courier allocation system, testing competencies in predictive modeling (per-stop service-time forecasting), constrained combinatorial optimization for routing and fairness, feature engineering, simulation, and production concerns such as cold-start handling and distribution shift. It is asked to assess how candidates balance competing objectives like on-time delivery versus fairness under real-world constraints (shift hours, vehicle capacity, delivery windows, time-varying travel forecasts), and is categorized under Machine Learning with a focus on practical application-level system design rather than purely conceptual theory.

Automatic Package-to-Courier Assignment with ML + Optimization

You previously assigned packages to couriers manually. Design an end-to-end system that automatically assigns packages per courier per shift, optimizing for both on-time delivery and fairness.

Provide the following:

Objective Function(s)

Define a primary objective (e.g., maximize on-time probability − λ·overtime − μ·distance) and fairness criteria (e.g., limit variance of workload or use CVaR fairness).

Constraints

Include: shift hours, vehicle capacity (weight/volume), delivery windows, geographic contiguity, skill requirements (e.g., hazmat), and realistic service-time behavior (from historical stop-time distributions).

Required Data Features

Specify features such as route density, travel-time forecasts by hour and weather, package size class, stop difficulty, and historical courier productivity.

Algorithmic Approach

Predict per-stop service time using ML (e.g., gradient boosting with prediction intervals) and feed predictions into a constrained optimizer (integer/linear programming, VRPTW, min-cost flow, or column generation).
Explain how you will handle cold-start couriers, guard against distribution shift, and design a fast rebalancing heuristic for last-minute spikes.

Pseudocode

Provide pseudocode for the end-to-end loop: forecast → optimize → simulate → assign → monitor.
Explain fallback behavior if the optimizer fails or times out.

Build a package-allocation model for couriers

Quick Overview

Automatic Package-to-Courier Assignment with ML + Optimization

Solution

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