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

Reconciling Conflicting Report Metrics

HardSQL & Data Manipulation00:00
Practice interviewer
In session
5 left
00:00

Your question is Reconciling Conflicting Report Metrics. 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

Explain your approach

Two ASI reports show different values for what appears to be the same metric. Describe how you would investigate the discrepancy using SQL and data manipulation techniques, including how you would validate metric definitions, compare filters, inspect join grain, detect duplicate inflation, and isolate the step where the numbers diverge.

What this tests

  • Reconciling conflicting report logic
  • Validating joins and aggregation grain
  • Using CTEs for side-by-side comparisons
  • Using window functions to detect duplicate or stale records
  • Communicating root cause clearly