Root Cause Analysis and Diagnostics Questions

Systematic methods, mindset, and techniques for moving beyond surface symptoms to identify and validate the underlying causes of business, product, operational, or support problems. Candidates should demonstrate structured diagnostic thinking including hypothesis generation, forming mutually exclusive and collectively exhaustive hypothesis sets, prioritizing and sequencing investigative steps, and avoiding premature solutions. Common techniques and analyses include the five whys, fishbone diagramming, fault tree analysis, cohort slicing, funnel and customer journey analysis, time series decomposition, and other data driven slicing strategies. Emphasize distinguishing correlation from causation, identifying confounders and selection bias, instrumenting and selecting appropriate cohorts and metrics, and designing analyses or experiments to test and validate root cause hypotheses. Candidates should be able to translate observed metric changes into testable hypotheses, propose prioritized and actionable remediation steps with tradeoff considerations, and define how to measure remediation impact. At senior levels, expect mentoring others on rigorous diagnostic workflows and helping to establish organizational processes and guardrails to avoid common analytic mistakes and ensure reproducible investigations.

HardTechnical

0 practiced

Explain the synthetic control method for creating a counterfactual when randomization is not available. Provide a step-by-step plan of data requirements, donor pool selection, validation (placebo tests), and how you would implement it in practice to estimate the impact of a remediation that was applied to one country.

EasyTechnical

0 practiced

Metrics drift and ambiguous definitions cause repeated RCAs. As a data analyst, propose a short checklist of metric-definition guardrails and checks you would use to avoid common mistakes (e.g., deduplication, identity resolution, time windows, null handling). Provide examples of two metrics where these guardrails are particularly important.

HardTechnical

0 practiced

Write high-level SQL or pseudocode to compute a Kaplan–Meier style retention curve (survival function) from a users_events table with columns (user_id, event_name, event_time). The retention event is 'active' and users are observed over a 90-day window. Describe how you would handle right-censoring for users who are active near the end of the observation window and how you'd compute per-day at-risk counts.

EasyTechnical

0 practiced

A daily revenue metric drops sharply for one day then returns to normal. List the data-quality checks you would run to determine whether this was a real business issue or an instrumentation/ETL problem. Provide concrete SQL or monitoring checks (for example: missing partitions, duplicate events, schema changes, sampling). Prioritize checks you would run in the first 30 minutes.

EasyTechnical

0 practiced

Describe how to construct a fishbone (Ishikawa) diagram for a sudden increase in churn. List 6-8 root categories you would include (for example: product, onboarding, pricing, marketing, technical), then explain how you would convert branches of the fishbone into testable, data-driven hypotheses with specific metrics to check.

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