Microsoft
Easy
Data ScientistDesign evaluation when A/B test is impossible
Analytics & Experimentation
1
0
February 9, 2026
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
Pinterest
Medium
Data ScientistDesign and Evaluate a Home Carousel
Analytics & Experimentation
1
0
January 14, 2026
Capital One
Medium
Data ScientistAssess 3.4M target and design experiments
Analytics & Experimentation
2
0
October 13, 2025
Meta
Medium
Data ScientistDesign and analyze A/B test with interference
Analytics & Experimentation
1
0
October 13, 2025
Meta
Hard
Data ScientistDiagnose sudden KPI drop with segmentation
Analytics & Experimentation
2
0
October 13, 2025
Disney
Hard
Data ScientistDiagnose and decide on watch-time drop
Analytics & Experimentation
2
0
October 13, 2025
Meta
Hard
Data ScientistDesign cluster-randomized test under network effects
Analytics & Experimentation
2
0
October 13, 2025
PayPal
Hard
Data ScientistDesign an A/B for ATO rule
Analytics & Experimentation
1
0
October 13, 2025
Meta
Hard
Data ScientistJustify building a new feature with evidence
Analytics & Experimentation
1
0
October 13, 2025
Meta
Hard
Data ScientistDesign an A/B test for WFH filter
Analytics & Experimentation
1
0
October 13, 2025
Visa
Hard
Software EngineerDesign an experiment assignment service
Analytics & Experimentation
2
0
August 11, 2025
Google
Medium
Data ScientistDecide confidence level and forecast video views
Analytics & Experimentation
1
0
August 5, 2025
Meta
Hard
Data ScientistDetermine Key Metrics and Design A/B Test for Ad Ranking
Analytics & Experimentation
2
0
August 4, 2025
Meta
Medium
Data ScientistConvince PM to Implement Duplicate Observation Tool
Analytics & Experimentation
2
0
August 4, 2025
Amazon
Medium
Data ScientistDiagnose Business Decline Using Key Data Metrics
Analytics & Experimentation
3
0
August 4, 2025
Meta
Medium
Data ScientistHow to evaluate similar-listing notification feature
Analytics & Experimentation
2
0
December 25, 2025
PayPal
Medium
Data ScientistEvaluate smart cart idea with hypotheses and experiment
Analytics & Experimentation
1
0
December 10, 2025
Meta
Hard
Data ScientistEstimate revenue of organic shopping tab
Analytics & Experimentation
2
0
October 13, 2025
Meta
Hard
Data ScientistDesign experiment with network and novelty effects
Analytics & Experimentation
2
0
October 13, 2025
CVS Health
Hard
Data ScientistDesign a flu-shot A/B/n campaign experiment
Analytics & Experimentation
2
0
October 13, 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.