Data Investigation and Root Cause Analysis Questions

Techniques and a structured process for diagnosing metric changes and anomalies 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 or instrumentation problem by checking data quality, event counts, sampling, and pipeline integrity. Describe slicing and decomposition strategies such as cohort 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; and correlate metric changes with product releases, experiments, marketing activity, or external events. Include how to combine quantitative findings with qualitative research such as user interviews, session replay, logs, and support tickets to strengthen causal inference. Finally, cover communicating concise findings and actionable recommendations to stakeholders, creating reproducible queries and monitoring dashboards or alerts, and mentoring junior analysts on a systematic investigation approach.