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
After running a feature experiment you see a small, statistically insignificant positive lift and some qualitative complaints. As PM, draft the key points you would include in a concise stakeholder communication: findings, uncertainty, immediate recommendations, and next steps.
HardTechnical
0 practiced
Design an experiment ramping plan for a risky UI change. Include initial sample sizes, ramp steps, guardrail metrics (revenue, error rates), stopping rules, monitoring cadence, and rollback criteria. Explain the rationale for chosen thresholds and escalation paths.
HardTechnical
0 practiced
An experiment shows a 0.8% statistically significant lift in a high-volume funnel. As PM, estimate the incremental monthly revenue if rolled out to all users. List assumptions, show the calculation from baseline traffic and conversion to incremental revenue, include confidence intervals and sensitivity to AOV and retention assumptions.
HardTechnical
0 practiced
You introduce a pro-rated subscription change flow that allows upgrades/downgrades with partial refunds. Design metric definitions and SQL logic to accurately compute MRR, churn, upgrades, downgrades, and avoid double-counting revenue across overlapping billing periods.
HardTechnical
0 practiced
Two dashboards show different daily active user (DAU) counts. Describe a step-by-step investigative process to find root causes using data lineage, sample row comparisons, query diffing, and checks for timezone, deduplication, filters and recent ETL changes. List systematic tests you'd run.
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