Business Impact Measurement and Metrics Questions

Selecting, measuring, and interpreting the business metrics and outcomes that demonstrate value and guide decisions. Topics include high level performance indicators such as revenue decompositions, lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction, as well as operational indicators like throughput, quality and system reliability. Candidates should be able to choose leading versus lagging indicators for a given question, map operational KPIs to business outcomes, build hypotheses about drivers, recommend measurement changes and define evaluation windows. Measurement and attribution techniques covered include establishing baselines, experimental and quasi experimental designs such as A B tests, control groups, difference in differences and regression adjustments, sample size reasoning, and approaches to isolate confounding factors. Also included are quick back of the envelope estimation techniques for order of magnitude impact, converting technical metrics into business consequences, building dashboards and health metrics to monitor programs, communicating numeric results with confidence bounds, and turning measurement into clear stakeholder facing narratives and recommendations.

EasyTechnical

0 practiced

You're responsible for monitoring a production image-classification inference service. List the top 10 metrics (operational + quality + business) you would include in a monitoring dashboard and for each explain why it matters and whether it is leading or lagging for business impact. Include at least one metric that measures latency, one for model quality drift, and one for business impact.

HardSystem Design

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Design SLOs and an operational measurement plan to track generative-AI safety (hallucination and toxicity). Define the metrics, sampling scheme for human annotation, automated detectors, thresholds for alerts, and compute sample size needed to detect a 50% relative reduction in hallucination rate from a 5% baseline with 80% power and alpha=0.05. Explain trade-offs in sampling frequency and annotation costs.

MediumTechnical

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You run 20 concurrent experiments across the product. Explain the multiple-testing problem in this context. Compare Bonferroni correction and Benjamini-Hochberg (BH) procedure and recommend which is more appropriate for business-facing metric decisions. Discuss the trade-offs between controlling family-wise error rate (FWER) vs false-discovery-rate (FDR) for product experimentation.

MediumTechnical

0 practiced

Compute the required per-arm sample size to detect a 5% relative change in a continuous metric. Baseline mean = 100, standard deviation = 30, desired MDE = 5% of mean (i.e., delta = 5), alpha=0.05 two-sided, power=0.8. Provide the Python code (or formula and numeric result) you would use and explain each term in the formula.

MediumTechnical

0 practiced

Attribution challenge: a generative AI assistant surfaces recommendations across a user's multi-step purchasing journey (discovery, consideration, checkout). Propose a practical attribution strategy to measure incremental impact of the assistant on conversions: compare rule-based methods (last-touch/first-touch), model-based (Shapley), and experimental (randomized holdout). For each approach describe pros/cons and suggest an implementable plan given limited engineering resources.

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