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

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Design an experiment (including hypothesis, population, treatment, metrics, and stopping criteria) to test whether a suspected data-labeling regression is causing a drop in model performance. Assume you can re-label a stratified sample within two days and run an offline evaluation.

EasyTechnical

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Describe the difference between correlation and causation in the context of model diagnostics. Give a concise example where a metric change is correlated with a deployment event but is not caused by it, and explain how you would test whether the relationship is causal.

EasyTechnical

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Define root cause analysis (RCA) specifically for AI systems and models. In your answer, cover: 1) what distinguishes RCA for AI from general software debugging, 2) the typical steps you would take when an ML model's key metric degrades in production, and 3) four common mistakes teams make when diagnosing AI problems.

MediumTechnical

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Given a production regression where false positives for a fraud model have increased, describe how you'd use fault tree analysis plus data slicing to isolate whether the cause is a recent feature change, label drift, adversarial behavior, or a scoring bug. Include specific evidence you would look for in logs and analytics.

HardTechnical

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A complex model evaluation shows correlated failures across several features; you suspect a latent confounder. Describe how you would use instrumental variables or difference-in-differences (DiD) approaches to tease out causal effects from observational data. Provide a concrete example and the assumptions required.

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