Meta
Easy
Data ScientistHow would you evaluate a new ads ranking algorithm?
Analytics & Experimentation
11
0
October 30, 2025
Root Cause Analysis Interview Questions
Root cause analysis questions test how you investigate unexpected metric movements and diagnose product or data issues.
Expect scenario-based questions like "DAU dropped 10% this week — how would you investigate?"
Interviewers evaluate your structured approach, ability to prioritize hypotheses, and how you communicate findings.
782Questions
76Companies
Easy 97 • Medium 41 • Hard 12Difficulty mix
Common root cause analysis patterns
- Structured investigation framework (confirm → segment → hypothesize → validate)
- Segmentation by platform, geography, user cohort, and device
- Checking data pipeline issues before investigating product changes
- Funnel decomposition to isolate where the drop occurs
- Correlation with external events (holidays, competitor launches, outages)
- Quantifying impact to prioritize investigation
Root cause analysis interview questions
Filter and sort
DoorDash
Hard
Data ScientistDecide and test a 20% discount strategy
Analytics & Experimentation
6
0
October 13, 2025
Capital One
Hard
Data ScientistEvaluate launching a vegan burger
Analytics & Experimentation
9
0
October 13, 2025
Anthropic
Hard
Software EngineerDesign a profiling plan for kernels
Analytics & Experimentation
10
0
September 6, 2025
Meta
Easy
Product AnalystDesign experiments and diagnose metric changes
Analytics & Experimentation
7
0
February 22, 2026
Roku
Medium
Data ScientistMeasure Speaker's Impact Using Propensity Score Matching
Analytics & Experimentation
36
0
July 12, 2025
DoorDash
Hard
Data ScientistDiagnose Causes of High Out-of-Stock Rate in Groceries
Analytics & Experimentation
9
0
August 4, 2025
Optiver
Hard
Software EngineerDesign and backtest a trading strategy
Analytics & Experimentation
4
0
August 13, 2025
Amazon
Medium
Data ScientistDiagnose Causes and Test Hypotheses for Metric Drop
Analytics & Experimentation
35
0
July 12, 2025
Uber
Hard
Data ScientistDesign station experiment with interference and rush-hour spillovers
Analytics & Experimentation
9
0
October 13, 2025
Figma
Hard
Software EngineerDefine and measure article trending
Analytics & Experimentation
10
0
September 6, 2025
Netflix
Medium
Data ScientistHow to Design Effective A/B Tests for Onboarding
Analytics & Experimentation
22
0
July 12, 2025
Uber
Hard
Data ScientistDesign an A/B test for promo-targeting models
Analytics & Experimentation
6
0
October 13, 2025
Airbnb
Medium
Data ScientistDefine Success Metrics and Experiment Plan for Product Development
Analytics & Experimentation
24
0
July 12, 2025
Shopify
Medium
Data ScientistAnalyze Causes of November and June Shopify Traffic Spikes
Analytics & Experimentation
6
0
August 4, 2025
LinkedIn
Hard
Data ScientistAssess LinkedIn Newsfeed Health
Analytics & Experimentation
27
0
July 12, 2025
DoorDash
Hard
Data ScientistDesign experiments for marketplace product changes
Analytics & Experimentation
4
0
February 9, 2026
Meta
Easy
Data ScientistDesign and evaluate a new group call feature
Analytics & Experimentation
8
0
December 8, 2025
Uber
Hard
Data ScientistDefine ride success metric for Uber
Analytics & Experimentation
8
0
October 13, 2025
Meta
Hard
Data ScientistDetermine Success Metrics for New Group Video-Call Feature
Analytics & Experimentation
30
0
July 12, 2025
Common mistakes in root cause analysis
- Jumping to a hypothesis before confirming the data is correct
- Not segmenting the data to isolate the affected population
- Confusing correlation with causation
- Investigating too many hypotheses at once without prioritization
- Presenting findings without quantifying the impact
How root cause analysis is evaluated
Show a structured, systematic approach rather than random guessing.
Prioritize hypotheses by likelihood and ease of validation.
Communicate your investigation as a clear narrative with supporting data.
Related analytics concepts
Root Cause Analysis Interview FAQs
How do you investigate a metric drop?
First confirm it is real (check data pipelines). Then segment by dimensions (platform, country, cohort). Check for external factors and recent deployments. Decompose the metric into sub-components to isolate where the drop occurs. Quantify the impact and propose next steps.