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 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.

Adjust YouTube Ad Scores Using Mixed-Effects Linear Regression | Google Interview Question

Adjust YouTube Ad Scores Using Mixed-Effects Linear Regression

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of mixed-effects (hierarchical) linear regression and the ability to adjust for rater bias, testing competencies in statistical modeling, fixed-versus-random effects distinctions, and handling hierarchical rating data.

Google

Jul 12, 2025, 6:59 PM

Data Scientist

Technical Screen

Machine Learning

Scenario

100 reviewers each rate the same 100 YouTube ads on a 1–10 scale.
Ratings may be systematically higher or lower for some reviewers (leniency/severity bias).

Goal

Produce unbiased ad scores (comparable on a common scale) by removing reviewer bias.

Task

Propose a modeling framework to adjust for reviewer bias and justify why a mixed-effects linear regression is appropriate.

Assumptions

Each rating is on an interval-like scale (1–10). Treating it as approximately continuous is acceptable; see alternatives for ordinal modeling.
We want ad-specific scores (so ads are fixed effects) and to treat reviewers as a sample from a larger reviewer population (random effects).

Solution

Show

Comments (0)

Loading comments...

Browse More Questions

More Machine Learning•More Google•More Data Scientist•Google Data Scientist•Google Machine Learning•Data Scientist Machine Learning