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.

EasyTechnical

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Define A/B testing key terms: null hypothesis, statistical power, Type I/II errors, and p-value. In one sentence each, explain why each term matters when measuring business impact.

MediumSystem Design

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Design an experiment (A/B test) to measure whether offering a one-time discount to new users increases 90-day LTV. Specify: primary and secondary metrics, sample size considerations, randomization unit, duration, and potential biases to guard against.

HardSystem Design

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You must evaluate whether a new recommendation algorithm increased average order revenue. The algorithm was rolled out to 30% of traffic by buckets. Describe an analysis pipeline (data validation, metric computation, statistical testing, and bias checks) to measure impact.

EasyTechnical

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Explain what a cohort analysis is. Give one concrete example of a cohort definition for a subscription product and describe the business question that cohort analysis would answer.

EasyTechnical

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A stakeholder asks: 'Will changing our onboarding flow increase retention by 5%?' In plain language, outline the steps you would take to answer this question using data (from hypothesis to deployment of experiment).

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