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

0 practiced

Create a reproducible checklist and code-snippet outline (pseudocode acceptable) to automatically re-run an investigation end-to-end given a saved model artifact hash, dataset snapshot hash, and pipeline config. Include steps for environment provisioning, deterministic seeding, and output verification.

MediumTechnical

0 practiced

You suspect selection bias in an offline evaluation because labeled data comes from users who opted into a beta program. Explain how selection bias can distort offline metrics, and propose two analytical techniques (e.g., weighting, matching) to adjust the evaluation and test whether measured degradation persists after adjustment.

HardTechnical

0 practiced

A customer reports repeated incorrect outputs from a generative model that are later traced to a corrupt segment of the training data added two weeks ago. Draft a full post-incident remediation and prevention plan that includes short-term containment, data rollback or repair strategies, retraining considerations, communication to stakeholders, and long-term pipeline improvements.

MediumTechnical

0 practiced

Implement (describe or write) a plan to detect model drift using a combination of population-level metrics and online hypothesis tests. Specify which tests you would run (e.g., KS-test, population mean shift tests), how often, and how you would control for false discovery when monitoring many features.

HardTechnical

0 practiced

Design a statistical approach to distinguish between a real model performance decline and a transient sampling fluctuation. Include how you'd use control charts, confidence intervals, and minimum detectable effect calculations, and explain how to set thresholds to avoid both false alarms and missed incidents.

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