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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
24 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.
MediumTechnical
22 practiced
Explain how you would use canary and shadow traffic to validate a suspected fix for an API regression impacting a key endpoint. Describe traffic routing, data comparison between canary/shadow and primary, monitoring metrics to compare, and rollback criteria. Also mention pitfalls to avoid when interpreting shadow results.
HardSystem Design
24 practiced
Design an observability and diagnostics pipeline for product metrics with these scale assumptions: 100M events/day, sub-minute freshness for key dashboards, and the ability to replay past 30 days of events. Describe components (schema registry, streaming validation, OLAP store, dashboarding, alerting, runbooks), SLOs for freshness and accuracy, and how PMs should use these tools during an RCA.
MediumTechnical
20 practiced
Using fault tree analysis, outline how you would model the root causes of recurring payment failures. Identify the top-level failure, intermediate gates (AND/OR), base events (for example, network error, gateway auth error, malformed payload), and describe how you would convert the FTA output into prioritized mitigations based on likelihood and business impact.
HardTechnical
23 practiced
A third-party payment provider upgraded its API and within 48 hours Region A saw a 2% revenue drop. As PM, outline an end-to-end RCA: how you would detect and quantify the impact, what logs and metrics you'd inspect, how you'd coordinate with the vendor and engineering for diagnostics and a temporary fallback, a communication plan for stakeholders and affected customers, and acceptance criteria to declare the issue resolved and revenue restored.

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