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Data Investigation and Root Cause Analysis Questions

Techniques and a structured process for diagnosing an unexpected change in a metric, dataset, or system signal using quantitative evidence complemented by qualitative signals. Candidates should demonstrate how to validate that an observed change is a real signal and not noise, or a reporting, instrumentation, or pipeline problem, by checking data quality, event or record counts, sampling, schema stability, and pipeline or data-flow integrity. Describe slicing and decomposition strategies such as cohort or population segmentation, geography and platform segmentation, feature-level analysis, time series decomposition to separate trend and seasonality, funnel and velocity analysis, retention analysis, and variance analysis. Explain how to form, prioritize, and test hypotheses; design diagnostic queries and tests using structured query language or equivalent tooling; and correlate the change with plausible triggers such as releases or deployments, configuration or schema changes, experiments, campaigns, upstream system incidents, or external events. Include how to combine quantitative findings with qualitative evidence such as interviews, logs, session or trace replay, support tickets, or incident timelines to strengthen causal inference. Finally, cover communicating concise findings and actionable recommendations to stakeholders, creating reproducible queries and monitoring dashboards, alerts, or runbooks, and mentoring others on a systematic investigation approach. This applies broadly to investigating anomalies in business metrics, product data, system or service health signals, financial figures, or model performance, not only one of these domains.

MediumTechnical
54 practiced
You see a sudden spike in churn. Describe a feature-level analysis approach to diagnose potential drivers: which features to inspect first, how to compare distributions, which statistical or model-based tools (e.g., SHAP, permutation importance) to use, and how to produce prioritized, testable hypotheses for product owners.
MediumTechnical
42 practiced
A new marketing campaign launched last week and signups spiked. Design an investigation plan to test whether that campaign explains the uplift: define metrics, attribution rules, SQL joins you would run between ad logs and user events, time windows, and robustness checks to rule out coincident causes.
MediumTechnical
52 practiced
Write SQL to produce a 12-week retention matrix: for each signup_week cohort, compute the percentage of users active in week 0..11 after signup. Include schema for users(id, signup_at) and events(user_id, occurred_at). Describe handling of timezone, partial weeks, and cohorts with small sample size.
MediumTechnical
53 practiced
Describe a structured approach to combine qualitative signals (user interviews, session replays, support tickets) with quantitative anomaly detection results to strengthen causal claims. Provide a short checklist for sampling, labeling, and integrating qualitative evidence into the statistical narrative.
HardTechnical
43 practiced
You are asked to lead adoption of a systematic RCA culture across analytics teams. Propose a training and onboarding plan (workshops, templates, runbooks), governance (ownership, review cadence), success metrics (e.g., mean-time-to-root-cause, reproducibility rate), and a scalable mentorship mechanism. Explain how you would measure adoption and iterate.

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