Metrics Analysis and Data Driven Problem Solving Questions

Skills for using quantitative metrics to diagnose and solve product or support problems. Candidates should be able to identify relevant key performance indicators such as customer satisfaction, response time, resolution rate, and first contact resolution, detect anomalies and trends, formulate and prioritize hypotheses about root causes, design experiments and controlled tests to validate hypotheses, perform cohort and time series analysis, evaluate statistical significance and practical impact, and implement and monitor data backed solutions. This also includes instrumentation and data collection best practices, dashboarding and visualization to surface insights, trade off analysis when balancing multiple metrics, and communicating findings and recommended changes to cross functional stakeholders.

EasyTechnical

0 practiced

Explain the difference between acquisition cohorts and behavioral cohorts. Provide concrete examples for when you'd use each, and describe a situation where behavioral cohorts provide clearer insight than acquisition cohorts.

HardTechnical

0 practiced

Your billing depends on unique active user counts. Discuss when HyperLogLog (HLL) approximate distinct counting is acceptable vs when exact distinct is required. Cover accuracy guarantees, reproducibility, auditability, parameter tuning (precision), and any regulatory or contractual considerations. How would you monitor HLL accuracy in production?

EasyTechnical

0 practiced

You see a 50% overnight drop in reported daily resolved tickets on the executive dashboard. Provide a prioritized checklist to determine whether this is a real business issue or a reporting/data problem. Include fast SQL checks, instrumentation checks, ETL and pipeline health checks, and quick visualizations you would run.

MediumTechnical

0 practiced

You have two years of daily active users showing weekly seasonality and marketing spikes. Describe step-by-step how you'd decompose the time series, detect anomalies, and forecast 90 days ahead. Include model choices (STL, Prophet, ARIMA), feature engineering for promotions/holidays, and evaluation metrics you would use to choose the best model.

MediumTechnical

0 practiced

Distinguish between statistical significance and practical significance. Provide a business example where a very small but statistically significant change should not trigger an operational decision. How would you present this nuance to stakeholders?

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