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.

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).

HardTechnical

25 practiced

Discuss confounding and collider bias in the context of decision analyses for upsell programs. Provide a concrete example where conditioning on product usage (a collider) could create a spurious association between a treatment (e.g., receiving a sales outreach) and upsell success, and propose diagnostics and practical steps to detect and mitigate collider bias.

HardTechnical

26 practiced

Describe how to perform causal inference to measure the effect of a product change on checkout conversion using: (a) difference-in-differences (DiD), (b) instrumental variables (IV), and (c) regression discontinuity design (RDD). For each method, list the core assumptions, required data elements, diagnostics to validate assumptions, and how you'd report effect estimates and uncertainty to stakeholders.

HardTechnical

37 practiced

Your analysts have produced multiple ad-hoc reports with conflicting explanations for a revenue dip. Outline a reproducibility checklist and an expedited review process you would run to converge on a single, defensible root cause. Include how to reconcile conflicting datasets, how to adjudicate disagreements, and how to preserve final artifacts for audit and future reference.

MediumTechnical

32 practiced

Given three tables: users(user_id, created_at), events(user_id, event_name, event_time), payments(user_id, payment_date, amount), write an ANSI SQL query that computes 30-day trial conversion rate by week-of-signup cohort and returns a 4-week retention/ conversion table. Make sure to deduplicate users by earliest signup and to attribute conversions to the signup cohort.

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