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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
0 practiced
Design an A/B test to evaluate whether a redesigned onboarding flow reduces time-to-first-purchase across multiple regions while controlling for seasonality. Include hypotheses, primary and guardrail metrics, sample size considerations or power calculation inputs, blocking/stratification strategy, and a pre-analysis plan that avoids p-hacking.
MediumTechnical
0 practiced
Analytics show users who watch an optional onboarding tutorial have a 20% higher 30-day retention than those who do not. How would you determine whether the tutorial causes higher retention or is simply correlated with more engaged users? Describe specific analyses (matching, regression with controls, pre/post analysis), possible confounders, and how you would design an experiment to test causality.
HardSystem Design
0 practiced
Design an event schema and ingestion strategy that supports idempotency, schema evolution, and consumer compatibility. Specify critical fields (for example event_id, timestamp, version, payload), ingestion-time validation, schema registry considerations, and how to provide a safe replay/backfill path without breaking downstream consumers.
EasyTechnical
0 practiced
Define what makes a metric 'actionable' versus 'vanity' in product analytics. Given these metrics for a subscription product: daily active users (DAU), time-in-app, feature X clicks, and revenue per DAU, explain which are leading and lagging indicators for monetization and what additional instrumentation or breakdowns would make each metric actionable for RCA.
MediumTechnical
0 practiced
Design a diagnostic funnel to investigate a sudden drop in purchases originating from product detail pages. List the specific events for each funnel step, recommended diagnostic metrics (step conversion, time between steps, error rates), and the queries or slices you would run to identify the step with the largest leak. Include monitoring thresholds you might configure.

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