Dataford
Interview QuestionsInterview GuidesExperiencesCoaching
New
Pricing
Get started
Dataford
Popular roles
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Browse
Browse All RolesEvery role hub, from analyst to MLBrowse All CompaniesCompany-specific interview loopsAll Interview GuidesThe full guide library
Top questions by role
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Top questions by skill
SQLPythonStatisticsMachine LearningA/B TestingSystem DesignGenerative AIProduct SenseMetricsBehavioral
Browse all questions →Try a mock interview
Experiences
Practice
Mock InterviewsTimed interview simulations with feedbackCoaching1:1 coaching with people at your target companiesSuccess PathYour 6-week structured planModulesCurated lessons by topicWebinarsTalks from ex-Big Tech data leadsPlaygroundA free-form scratch editor
Learn
BlogInterview strategy and career adviceTech Job Market ReportHiring trends across data and AI rolesFor UniversitiesDataford for career centersAbout DatafordWho we are and how we build
Pricing
Build my plan

CUPED for Mobile Feature Test

HardA/B Testing & Experimentation00:00
Practice interviewer
In session
5 left
00:00

Your question is CUPED for Mobile Feature Test. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

You need to log in / sign up to chat or submit.

Problem

Scenario

You are analyzing an A/B test for a new mobile gameplay feature in a live game. The outcome metric is noisy because player activity varies a lot day to day, and you want to improve sensitivity without changing the product experience or rerunning the test.

Question

How would you use CUPED or another variance-reduction approach in this experiment analysis? Explain when it is appropriate, how you would implement it, and what checks you would run to make sure the estimated treatment effect remains valid.

Metric Setup

MDE·3 minutes absolute (2.5% relative)Baseline·120 minutesPrimary metric·7-day engaged playtime per playerRandomization unit·player_id

Key Risks

  • Noisy engagement metric
  • Potential sample ratio mismatch
  • Possible unit mismatch between sessions and players
  • Need to preserve unbiased treatment effect estimation