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.

HardTechnical

45 practiced

Propose a programmatic approach to correlate external events (ad campaigns, holidays, outages, social media spikes) with metric anomalies. Describe how you'd ingest external event signals, align time windows, control for confounders, and quantify the likelihood an external event caused the observed change.

HardSystem Design

47 practiced

Design an end-to-end reproducibility playbook for RCA investigations: include dataset versioning approach (e.g., data hashes, Delta Lake / S3 snapshots), seed and env control for models, artifact storage, and audit logging. Provide concrete steps an analyst must follow to produce a reproducible RCA deliverable.

EasyTechnical

47 practiced

You have a time series of a product metric for the last 90 days. Explain three simple statistical tests or visual checks you would perform to decide if a recent dip is statistically significant versus expected random fluctuation. Mention assumptions and limitations of each test.

HardTechnical

57 practiced

Explain how causal inference techniques such as difference-in-differences (DiD), synthetic controls, and instrumental variables could be used during RCA when randomized experiments are not available. For each technique, give a short scenario where it would be appropriate and list its main assumptions and potential pitfalls.

HardTechnical

44 practiced

A distributed A/B experiment was rolled out to multiple regions and treatment effect estimates differ in sign across regions. How would you reconcile these results? List checks to perform to detect heterogeneity of treatment effect vs instrumentation/assignment problems and statistical approaches to summarize global impact.

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