Data Driven Recommendations and Impact Questions

Covers the end to end practice of using quantitative and qualitative evidence to identify opportunities, form actionable recommendations, and measure business impact. Topics include problem framing, identifying and instrumenting relevant metrics and key performance indicators, measurement design and diagnostics, experiment design such as A B tests and pilots, and basic causal inference considerations including distinguishing correlation from causation and handling limited or noisy data. Candidates should be able to translate analysis into clear recommendations by quantifying expected impacts and costs, stating key assumptions, presenting trade offs between alternatives, defining success criteria and timelines, and proposing decision rules and go no go criteria. This also covers risk identification and mitigation plans, prioritization frameworks that weigh impact effort and strategic alignment, building dashboards and visualizations to surface signals across HR sales operations and product, communicating concise executive level recommendations with data backed rationale, and designing follow up monitoring to measure adoption and downstream outcomes and iterate on the solution.

MediumTechnical

0 practiced

An A/B test shows a positive lift for the overall population but a negative effect for new users (a critical segment). Outline the statistical and operational steps you would take before deciding to roll back, including subgroup power checks, replication, and mitigation options.

MediumTechnical

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You rolled out a recommender to only high-value customers initially; now you must estimate the causal effect of the recommender on revenue using observational data where treatment assignment is confounded by customer value. Describe a step-by-step approach (methods, diagnostics) to estimate a credible causal effect.

EasyTechnical

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You must present to executives: an experiment shows a +2% relative CTR improvement but page latency increased by 20% and CPU costs rose 15%. Prepare a concise executive-level recommendation: state your decision (rollout/hold), key assumptions, trade-offs, and proposed next steps including monitoring and mitigation.

EasyTechnical

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Explain the difference between correlation and causation in the context of product metrics and personalization. Give two concrete examples where correlation would be misleading when evaluating a recommender, and one experimental approach to resolve each example.

MediumSystem Design

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Design how you would instrument feature flags and model_version fields so experiments and rollbacks are safe. Include event tagging conventions, schema for downstream attribution, and steps to guarantee that a rollback will not corrupt historical analytics or attribution data.

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