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 Meta.

What role is this question designed for?

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

Evaluate Factors Before Replacing Recommendation Model | Meta Interview Question

Evaluate Factors Before Replacing Recommendation Model

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's competency in machine learning model evaluation, online experimentation, business-metric trade-offs, and stakeholder/executive communication required for responsibly replacing an ads recommendation/ranking model.

Ads Model Replacement: Evaluation, Trade-offs, Experimentation, and Executive Readout

Scenario

A large ads platform has built a new recommendation/ranking model for ads and plans to deprecate the existing one. You are asked to evaluate and launch the new model responsibly.

Questions

(a) What factors must be evaluated before fully replacing the existing model?

(b) If CTR falls but revenue or other business metrics increase, how would you decide which model to ship?

(d) How would you visualize and present the final results to the CFO?

Hints: Cover offline evaluation, guardrails, rollback plans, stakeholder dashboards, and statistical significance.

Solution

Show

Comments (0)

Loading comments...

Browse More Questions

More Machine Learning•More Meta•More Data Scientist•Meta Data Scientist•Meta Machine Learning•Data Scientist Machine Learning