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

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

Build Classifier: Evaluate with AUROC for Imbalanced Data

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in building and evaluating binary classifiers for imbalanced datasets, focusing on model design, evaluation metric selection, and performance interpretation.

Google

Jul 12, 2025, 6:59 PM

Data Scientist

Technical Screen

Machine Learning

Detecting Dead Links: Build and Evaluate a Classifier

Scenario

You have a dataset of 1,000 URLs labeled as good (alive) or bad (dead). The classes are likely imbalanced (e.g., far fewer dead links than good ones).

Task

Describe how you would build the classifier end-to-end (data prep, features, model, validation, and deployment considerations).
Explain which evaluation metric(s) you would choose for imbalanced data.
Clarify why AUROC might be preferred over accuracy when the classes are imbalanced.

Hint: A strong baseline is logistic regression with class-imbalance-aware metrics.

Solution

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