Problem Definition and Hypothesis Formation Questions

Break down ambiguous business questions into specific, answerable analytics problems and define what success looks like. Ask clarifying questions about business context, constraints, stakeholder expectations, and acceptance criteria. Use structured diagnosis and root cause analysis to isolate where a problem occurs by segmenting users, products, time periods, or geographies. Generate multiple testable hypotheses that explain observed outcomes, distinguish correlation from causation, and prioritize hypotheses by likelihood, potential impact, and ease of validation. Frame measurable metrics for each hypothesis and propose high level validation approaches or experiments to confirm or reject the hypotheses.

HardSystem Design

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Design a reproducible cross-team framework for hypothesis tracking and validation. Include templates for hypothesis statements, required data contracts, experiment artifacts, versioned notebooks or queries, automated test suites, and governance for sign-off and auditing. Discuss trade-offs between strict governance and team agility.

MediumTechnical

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Explain how model explainability tools such as SHAP can be used to generate causal hypotheses about model failures. Describe two specific pitfalls when interpreting feature importance as causal evidence and propose mitigations for each pitfall.

MediumTechnical

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You hypothesize that a new product variant is causing increased returns among high-value customers, but labeling returns is expensive and slow. How would you prioritize this hypothesis and design a low-cost validation plan using sampling, proxy signals, or active learning to gather evidence quickly?

MediumTechnical

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Offline AUC improved after retraining, but online CTR dropped after deployment. Propose at least five testable hypotheses to explain this discrepancy and describe the analysis or experiment you would run to validate each hypothesis, including which offline and online metrics to compare.

EasyTechnical

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A stakeholder gives the ambiguous brief: 'improve user retention'. List the clarifying questions you would ask to convert this into a concrete analytics problem. Cover baseline metrics, timeframe, target segments, business constraints, acceptable lift thresholds, KPIs to optimize, and any privacy or regulatory constraints. Conclude with one example measurable objective (metric, baseline, target, timeframe) you would propose.

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