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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.

MediumSystem Design
22 practiced
Design an A/B test to determine whether a recent pricing-page UI change caused a drop in trials converting to paid. Specify: primary and secondary metrics, randomization unit, basic sample size/power considerations, experiment length, instrumentation checks, guardrails, and kill criteria.
MediumTechnical
25 practiced
Describe how you would onboard and mentor a new revenue analyst on rigorous diagnostic workflows. Provide a 30/60/90 day plan, key artifacts they should produce (e.g., reproducible notebooks, RCA templates), review cadence, and the guardrails you'd enforce to catch common analytic mistakes.
EasyTechnical
26 practiced
Explain the difference between correlation and causation. Provide two realistic revenue-ops examples where a naive correlation could mislead stakeholders: one where correlation is spurious and one where an observed correlation masks a causal effect. For each example, describe additional analyses or evidence you would collect to test causality.
EasyTechnical
26 practiced
List the minimum set of telemetry events and properties you would instrument on a B2B SaaS signup-to-paid funnel to enable robust root-cause analysis of conversion problems. For each event/property, explain why it matters and give one example diagnostic query it would enable (e.g., 'pricing-view' with plan-id enables cohorting by pricing variant).
EasyTechnical
24 practiced
Given a 'subscriptions' table with columns: customer_id, period_start (date), period_end (date), mrr (numeric), and status (active|cancelled), write an ANSI SQL query that calculates each customer's MRR by calendar month, computes month-over-month percent change using LAG(), and flags rows where percent change <= -0.5 (a 50% or greater drop). Assume missing months should be treated as zero MRR.

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