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 easy 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.

Design an ad recommendation ranking approach

Last updated: Mar 29, 2026

Quick Overview

This question evaluates competency in machine-learning driven ad ranking and recommendation systems, including objective formulation, modeling strategy (feature and label design, candidate generation versus ranking), offline and online evaluation, experimentation, and production challenges such as cold start, budget pacing, feedback loops, and calibration. It is commonly asked because it assesses the ability to balance long-term business value with user experience, reason about metrics and failure modes, and design reliable evaluation and experimentation pipelines; the domain is Machine Learning and the level of abstraction spans both conceptual understanding and practical application.

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Design an ad recommendation ranking approach

Goal

Maximize long-term business value while maintaining a good user experience.

Prompt

Describe how you would:

Formulate the ranking objective (e.g., revenue vs engagement tradeoff)
Build a modeling approach (features, labels, candidate generation vs ranking)
Evaluate offline (datasets, metrics, counterfactual concerns)
Run online experiments (A/B design, guardrails)
Handle practical challenges: cold start, budget/pacing, feedback loops, and calibration

Be specific about key metrics and failure modes.

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