Data Problem Solving and Business Context Questions
Practical data oriented problem solving that connects business questions to correct, robust analyses. Includes translating business questions into queries and metric definitions, designing SQL or query logic for edge cases, handling data quality issues such as nulls duplicates and inconsistent dates, validating assumptions, and producing metrics like retention and churn. Emphasizes building queries and pipelines that are resilient to real world data issues, thinking through measurement definitions, and linking data findings to business implications and possible next steps.
MediumTechnical
0 practiced
For a revenue metric with a long-tailed distribution, describe how you would surface a representative central tendency on a dashboard and explain multiple approaches (mean, median, trimmed mean, winsorizing, log-transform). Discuss tradeoffs and how you would present both raw and adjusted metrics to stakeholders.
MediumTechnical
0 practiced
Write reconciliation SQL checks that validate daily revenue aggregates: compare sum(amount) from orders table to sum(amount) from payments table by date. Provide a query pattern to flag dates where absolute or relative mismatch exceeds thresholds and explain handling of timezone mismatch and late payments.
MediumTechnical
0 practiced
A satisfaction survey shows an unexpectedly high Net Promoter Score (NPS). Explain possible sampling biases that could cause this result (response bias, selection bias, survivorship bias) and describe statistical techniques or weighting strategies to correct for biased respondent samples so your estimates better reflect the user population.
HardSystem Design
0 practiced
A user exercises GDPR 'right to be forgotten'. How would you design pipelines and metric computation so you can honor deletion requests while maintaining reproducible analytics and minimizing disruption to dashboards? Discuss soft-delete vs physical delete, pseudonymization, audit trails, and implications for historical aggregates.
HardTechnical
0 practiced
Write production-quality SQL to compute rolling 30-day retention cohorts where 'active' is defined as either 'login' or 'purchase' events. Handle deduplication, late-arriving events, and users with multiple identities merged post-hoc. Also describe how you would test and validate the output against a small hand-crafted sample.
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