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.

MediumTechnical

0 practiced

Explain time series decomposition (trend, seasonality, residual) and describe how you would use decomposition to diagnose a KPI regression that may be due to seasonal effects, a trend change, or an unusual residual. Mention libraries or tools you would use (e.g., statsmodels, Prophet, tsfresh) and how to interpret results.

HardTechnical

0 practiced

A product launch coincided with a 15% drop in 6-month customer lifetime value (LTV). As a senior BI analyst, outline a causal analysis plan to identify whether the launch feature or confounded changes (pricing, marketing mix, or acquisition channels) caused the drop. Include required data, proposed statistical methods, experiments to run, remediation options, and how to measure long-term impact.

MediumTechnical

0 practiced

A product manager claims a recent UX change caused revenue to drop. You have event logs, frontend metrics, and partial server logs, but no randomized experiment. Describe a step-by-step plan to evaluate whether the UX change caused the drop, including observational checks, strategies to control for confounders, and how to present uncertainty and confidence to stakeholders.

MediumTechnical

0 practiced

You have 12 plausible hypotheses for a KPI drop and only two analysts available for 48 hours each. Describe a quantitative and qualitative prioritization framework you would use to select which hypotheses to test first. Include factors like impact, confidence, effort, and data availability, and illustrate with a short example scoring table (Impact, Confidence, Effort).

EasyTechnical

0 practiced

List and explain common data-quality checks you would run before starting an RCA on a revenue metric drop. Include checks for completeness, freshness, duplicates, schema drift, outliers, and upstream pipeline failures. For two checks, provide example SQL assertions you might run.

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