Meta
Easy
Data ScientistHow to evaluate similar-listing notifications feature
Analytics & Experimentation
113
0
January 17, 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.
772Questions
75Companies
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
Meta
Easy
Data ScientistHow would you evaluate emoji reactions launch?
Analytics & Experimentation
79
0
February 21, 2026
Airbnb
Hard
Data ScientistDesign an A/B test with causal inference
Analytics & Experimentation
59
0
October 13, 2025
DoorDash
Medium
Data ScientistEvaluate Impact of Bicycle Deliveries on Efficiency and Costs
Analytics & Experimentation
146
3
July 12, 2025
Lyft
Hard
Data ScientistAnalyze Causes of Increased Lyft Ride Wait Times
Analytics & Experimentation
133
1
August 4, 2025
Uber
Easy
Data ScientistMeasure feature impact with switchback, PSM, and CACE
Analytics & Experimentation
48
0
December 11, 2025
Meta
Medium
Data ScientistEvaluating a 15 % reduction in post‑card height
Analytics & Experimentation
144
1
July 12, 2025
TikTok
Hard
Data ScientistDesign A/B Test for Cost-Per-Conversion Efficiency Analysis
Analytics & Experimentation
131
2
August 4, 2025
Meta
Hard
Data ScientistDesign an Experiment to Evaluate New Recommendation Model
Analytics & Experimentation
123
2
August 4, 2025
Meta
Medium
Data ScientistEvaluating and launching Instagram Stories
Analytics & Experimentation
64
0
July 12, 2025
Coinbase
Medium
Data ScientistDiagnose Discrepancy in A/B Test Conversion Rate Results
Analytics & Experimentation
116
2
July 12, 2025
Meta
Medium
Data ScientistDecide and experiment on Group Call feature
Analytics & Experimentation
33
0
October 13, 2025
DoorDash
Hard
Data ScientistDesign and analyze a switchback experiment
Analytics & Experimentation
35
0
October 13, 2025
OpenAI
Medium
Data ScientistAnalyze A/B Test Results for Subscription Conversion Rates
Analytics & Experimentation
95
1
July 12, 2025
DoorDash
Medium
Data ScientistInvestigate Causes of Cold Meal Deliveries
Analytics & Experimentation
35
0
July 12, 2025
Lyft
Hard
Data ScientistInvestigate Causes and Effects of Dynamic Pricing on ETAs
Analytics & Experimentation
83
0
July 12, 2025
Meta
Medium
Data ScientistEvaluate the Success of Instagram Checkout
Analytics & Experimentation
90
0
July 12, 2025
Uber
Hard
Data ScientistDesign an Uber A/B experiment end-to-end
Analytics & Experimentation
32
0
October 13, 2025
Roblox
Easy
Data ScientistHow to estimate feature impact on usage time
Analytics & Experimentation
33
0
January 17, 2026
Meta
Easy
Data ScientistEvaluate new shop-ads ranking algorithm
Analytics & Experimentation
30
0
January 17, 2026
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.