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Data and Business Outcomes Questions

This topic focuses on converting data analysis and insights into actionable business decisions and measurable outcomes. Candidates should demonstrate the ability to translate trends into business implications, choose appropriate key performance indicators, design and interpret experiments, perform cohort or funnel analysis, reason about causality and data quality, and build dashboards or reports that inform stakeholders. Emphasis should be on storytelling with data, framing recommendations in terms of business levers such as revenue, retention, acquisition cost, and operational efficiency, and explaining instrumentation and measurement approaches that make impact measurable.

HardTechnical
0 practiced
You are the BI lead responsible for metric governance across multiple products. Design a policy for canonical metric definitions, versioning, access controls, and change management that prevents dashboard breakage and conflicting numbers. Include tooling and communication processes.
HardTechnical
0 practiced
Write efficient SQL to compute cohort retention curves and compute each cohort's retention half-life (the week where retention falls to 50% of its week 0). Schema: users(user_id, signup_date) and activity(user_id, activity_date). Explain computational complexity and optimizations for BigQuery/Redshift.
MediumTechnical
0 practiced
A critical dashboard query with several joins and window functions takes minutes to run. List concrete steps you would take to diagnose and optimize it (indexes/partitions, rewrite queries, materialized views, pre-aggregations). Provide an example of a query rewrite that reduces computation.
MediumTechnical
0 practiced
You observe a sudden spike in refund rates. Outline a root-cause analysis plan: what segments to analyze, SQL queries or visualizations to run, how to check for instrumentation bugs vs genuine business issues, and how to prioritize next actions.
MediumTechnical
0 practiced
Explain correlation vs causation and describe a practical scenario where difference-in-differences (DiD) would be appropriate to estimate causal impact. Sketch the basic DiD regression and key identification assumptions you would test.

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