Business Problem Solving and Recommendations Questions

Frameworks and skills for taking ambiguous business questions through analysis to clear, actionable recommendations. Includes decomposing complex problems into analyzable components, identifying key drivers, selecting focused analyses, synthesizing data backed findings, and articulating specific next steps and implementation considerations. Emphasizes communicating recommendations in business terms, estimating potential impact when possible, acknowledging trade offs and limitations, prioritizing among multiple actions, and tailoring communication to different stakeholders. Covers translating research or analytic results into feasible product or operational changes and defending choices with evidence.

EasyTechnical

51 practiced

List and explain five essential data quality checks you would run before trusting product analytics for decision-making. For each check, describe how you'd detect the issue in SQL or Python and one practical remediation or assumption you might apply (e.g., drop, impute, or flag).

HardTechnical

60 practiced

Design an experimentation strategy for rolling out a product change across multiple countries with different baselines, sample sizes, and regulatory constraints. Describe how you would pre-specify analyses, pool results (fixed vs. random effects / meta-analysis), adjust for heterogeneity, define stopping rules, estimate heterogeneous treatment effects, and decide on global vs. local rollouts.

MediumTechnical

69 practiced

Design an A/B test to evaluate a redesigned checkout flow intended to increase conversion. Specify: primary metric, secondary/guardrail metrics, sample-size estimation (show the formula and example calculation), randomization strategy, experiment duration, monitoring plan and stopping rules, and how to handle multiple variants and novelty effects. Assume 100K weekly users, baseline conversion 2%.

MediumSystem Design

53 practiced

Design a dashboard to help execs decide between two initiatives: (A) improve onboarding flow, (B) invest in a new marketing channel. Specify primary metrics, supporting cohorts and segments, recommended visualizations (with rationale), decision thresholds, required drill-downs, and a template one-page recommendation summary. Explain how you would present uncertainty and trade-offs on the dashboard.

HardTechnical

62 practiced

Design an approach to attribute multi-touch conversions across channels when data is incomplete (e.g., missing ad impressions for some users) and sampling bias is present. Compare rule-based attribution, fractional models, and data-driven probabilistic attribution (Markov chains, Shapley), discuss assumptions and data requirements for each, and recommend how to use the outputs to inform channel investment.

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