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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.

HardTechnical
22 practiced
You have 10 plausible root cause hypotheses for a 15% MAU drop but only two analytics engineers and a 2-week window. Propose a prioritization rubric (include criteria like impact, confidence, effort, detectability) and apply it to three example hypotheses: (A) front-end bug causing navigation failures, (B) paid-acquisition channel paused, (C) a pricing change confusion. Show the prioritized investigation plan with owners and time-boxed steps.
HardTechnical
26 practiced
A bug in the analytics ETL caused revenue events to be dropped for the last 72 hours. As PM, outline the steps you would take to: 1) quantify the missing revenue, 2) decide whether to backfill the missing data, 3) prioritize which downstream dashboards and reports must be recomputed, and 4) verify that the backfill is correct. Mention trade-offs of immediate backfill vs delayed recompute.
HardTechnical
24 practiced
Design a company-level RCA playbook and guardrails for an organization of roughly 200 engineers and 25 PMs to ensure investigations are reproducible and analytically sound. Include required artifacts, roles, expected timelines, tooling (notebooks, dashboards), peer review steps, and explicit guardrails to avoid common analytic mistakes such as p-hacking or data leakage.
MediumTechnical
20 practiced
Draft a concise triage checklist PMs should follow when alerted to a significant product metric regression. The checklist should enable quick impact assessment, stakeholder notification, initial hypotheses, top-of-stack data validation steps, temporary mitigations, and criteria for escalating to on-call engineering. Keep it short enough to execute in the first 30 minutes.
HardTechnical
26 practiced
Given these variables: exposed_to_promo, user_income, ad_clicks, purchase, and region, draw a causal directed acyclic graph (DAG) that captures plausible confounders when estimating the effect of the promo on purchase. Explain which variables you should condition on, which are colliders to avoid conditioning on, and how conditioning choices affect your estimates.

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