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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.

EasyTechnical
35 practiced
Describe how to construct a fishbone (Ishikawa) diagram for a sudden increase in churn. List 6-8 root categories you would include (for example: product, onboarding, pricing, marketing, technical), then explain how you would convert branches of the fishbone into testable, data-driven hypotheses with specific metrics to check.
HardTechnical
24 practiced
You're running hundreds of cohort slices and ad-hoc tests each week. Describe practical strategies to control the false discovery rate and avoid chasing spurious slices. Discuss statistical corrections (Bonferroni, Benjamini-Hochberg), organizational guardrails (pre-registration, exploratory vs confirmatory tagging), and tooling changes to minimize false leads.
MediumTechnical
20 practiced
Design an A/B test to validate a candidate root cause that you suspect is increasing checkout abandonment. Define: treatment assignment, primary and guardrail metrics, pre-registration analysis plan, sample size/power (describe how you'd compute it), monitoring rules, and a rollback criterion. Mention implementation details that prevent common analytic mistakes.
MediumTechnical
32 practiced
You have 4 competing hypotheses for a sudden revenue drop: (A) payment gateway latency, (B) UI change confusing users, (C) marketing channel mix change, (D) bot traffic spikes. Propose a prioritized investigation plan (which hypothesis first and why), a minimal set of data queries or checks to validate each hypothesis quickly, and what quick mitigations you might recommend while investigating.
HardTechnical
23 practiced
Propose a set of standards and tooling for reproducible RCA analyses in your analytics org. Cover version control for SQL and notebooks, parameterized notebooks or scripts, CI checks for metric SQL, data snapshotting/versioning, data lineage, packaging analyses as tests, and how to make analyses discoverable and re-runnable by others.

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