PracHub
QuestionsPremiumCoachesLearningGuidesInterview Prep
|Home/Analytics & Experimentation/Meta

Evaluate New Ad Model with A/B Testing Experiment

Last updated: Mar 29, 2026

Quick Overview

This question evaluates experimental-design and product-analytics competencies—A/B testing, traffic allocation and ramping, metric definition and guardrails, statistical power and significance, and handling marketplace/auction interference—within the Analytics & Experimentation domain for a Data Scientist role.

  • hard
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Evaluate New Ad Model with A/B Testing Experiment

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Onsite

##### Scenario As part of the Ads team you have trained a new ad-recommendation model and need to decide if it should replace the current model. ##### Question Design an experiment to evaluate the new recommendation model against the incumbent. What primary and guardrail metrics would you track? How would these differ when presenting results to the CFO versus the CGO (growth)? State your final recommendation framework for launch or rollback. ##### Hints Cover experiment design (A/B, traffic split, duration), metrics (revenue, CTR, ROI, user retention), statistical significance and trade-offs for finance vs growth.

Quick Answer: This question evaluates experimental-design and product-analytics competencies—A/B testing, traffic allocation and ramping, metric definition and guardrails, statistical power and significance, and handling marketplace/auction interference—within the Analytics & Experimentation domain for a Data Scientist role.

Related Interview Questions

  • Measure scheduled posts feature success - Meta (medium)
  • Estimate ads ranking revenue impact - Meta (medium)
  • How should you evaluate unconnected content? - Meta (medium)
  • Should WhatsApp launch group calls? - Meta (medium)
  • How would you grow Meta products? - Meta (medium)
Meta logo
Meta
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
2
0

Evaluate a New Ads Recommendation Model via Online Experimentation

Scenario

You have trained a new ad-recommendation model and must decide whether it should replace the incumbent model that currently ranks/serves ads in a large-scale, auction-based ads system.

Task

Design an experiment to evaluate the new model against the incumbent and answer:

  1. Experiment design: unit of randomization, traffic split/ramp, duration, and how to handle auction/marketplace interference.
  2. Metrics: define primary success metric(s) and guardrails (revenue, CTR, ROI, user retention, latency, etc.).
  3. Stakeholder readouts: how the story and metrics differ for a CFO vs a CGO (growth).
  4. Decision framework: launch or rollback criteria, with monitoring and risk mitigation.

Requirements

  • Cover A/B design, traffic allocation, expected duration, statistical significance/power, and trade-offs between finance and growth.
  • Include clear metric definitions and assumptions where needed.

Solution

Show

Submit Your Answer to Earn 20XP

Sign in to leave a comment

Loading comments...

Browse More Questions

More Analytics & Experimentation•More Meta•More Data Scientist•Meta Data Scientist•Meta Analytics & Experimentation•Data Scientist Analytics & Experimentation
PracHub

Master your tech interviews with 8,000+ real questions from top companies.

Product

  • Questions
  • Learning Tracks
  • Interview Guides
  • Resources
  • Premium
  • For Universities
  • Student Access

Browse

  • By Company
  • By Role
  • By Category
  • Topic Hubs
  • SQL Questions
  • Compare Platforms
  • Discord Community

Support

  • support@prachub.com
  • (916) 541-4762

Legal

  • Privacy Policy
  • Terms of Service
  • About Us

© 2026 PracHub. All rights reserved.