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 Accurate Energy Consumption Prediction Model for Utilities

Quick Overview

This question evaluates a data scientist's mastery of end-to-end supervised regression for time-indexed panel data, including time-series and seasonal feature engineering, weather-dependent modeling, robust baselines, time-aware cross-validation, residual analysis, leakage prevention, and production concerns such as deployment, monitoring, and retraining triggers. It is commonly asked in Machine Learning interviews to assess practical application skills in building production-ready predictive systems rather than purely conceptual understanding, emphasizing model validation, operational monitoring, and handling of panel/time-series challenges.

Predicting Daily Energy Consumption: End-to-End Regression to Production

Context

You need to build and productionize a supervised regression model that predicts next-day daily energy consumption (kWh) for utility clients (a panel of customers/meters). The data is time-indexed and exhibits strong seasonality and weather dependence.

Task

Describe, in order, the exact steps you would take from raw data to a monitored production system, covering:

Data acquisition and definition of the prediction problem
Data quality checks and exploratory data analysis (EDA)
Feature engineering
Baselines and model selection
Training, validation, and cross-validation design
Model evaluation and residual analysis
Productionization (pipelines, deployment)
Monitoring, alerting, and retraining triggers

Requirements

Mention strong baselines (e.g., yesterday/last-week, seasonal averages)
Use appropriate cross-validation for time series/panel data
Address feature scaling and encoding
Perform residual analysis and guard against leakage
Define concrete retraining triggers