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This is a Medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Google.

What role is this question designed for?

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

Build and evaluate bad-link classifier

Last updated: Apr 17, 2026

Quick Overview

This question evaluates proficiency in applied machine learning classification, including feature design, training a logistic regression, handling severe class imbalance, selecting evaluation metrics and calibration, choosing thresholds under asymmetric costs, and planning offline-to-online validation and monitoring.

Google

Oct 13, 2025, 9:49 PM

Data Scientist

Technical Screen

Machine Learning

You have 1,000 URLs labeled as bad or good and a much larger unlabeled pool, with bad links rare. Design features and train a logistic regression. Explain your evaluation plan under class imbalance: stratified K-folds, ROC-AUC vs PR-AUC, calibration (reliability curves), and why accuracy is misleading. Choose a decision threshold by minimizing expected misclassification cost given asymmetric costs. Discuss class weighting or resampling, leakage checks, monitoring for dataset shift between labeled and production traffic, and an offline-to-online validation plan with shadow or canary deployment.

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