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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
19 practiced
A KPI dropped globally but some regions showed larger declines. Propose a prioritized list of investigative steps and specific queries/visualizations you would run to identify whether an issue is region-specific. Describe how you would determine if the root cause is product, marketing, or data-related.
MediumTechnical
23 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).
MediumTechnical
19 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.
HardTechnical
26 practiced
Explain difference-in-differences (DiD), instrumental variables (IV), and regression discontinuity (RD) in the context of product analytics. For each method, provide a BI use-case, the key assumptions required, common pitfalls, and simple diagnostics or visualizations to check assumptions.
HardTechnical
18 practiced
You have a clickstream dataset with tens of billions of rows in BigQuery: (user_id, timestamp, event_type, country, device). Describe an efficient approach—SQL patterns, pre-aggregation, partitioning, materialized views, and approximate algorithms—to compute funnel conversion per weekly cohort for the last 90 days with query latency under 2 minutes and reasonable cost. Discuss trade-offs between exactness and latency.

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