Metrics and KPI Fundamentals Questions

Core principles and practical fluency for defining, measuring, and interpreting metrics and key performance indicators. Candidates should be able to select meaningful metrics aligned to business objectives rather than vanity metrics, explain the difference between a metric and a target, and distinguish leading indicators from lagging indicators. Coverage includes decomposing complex outcomes into actionable component metrics, writing precise metric definitions such as what counts as a daily active user and monthly active user, calculating common metrics such as engagement rate, churn rate and conversion rates, establishing baselines and sensible targets, and interpreting signal versus noise including awareness of statistical variability. Also includes using segmentation and cohort analysis to diagnose metric movements and recommending two to three meaningful metrics for a hypothetical problem with justification and action plans.

HardTechnical

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You are asked to recommend two to three meaningful metrics for the problem: "New user activation is low for our freemium mobile app." For each metric, provide precise definitions (numerator, denominator, window), explain why it matters, and propose one engineering or product action tied to improving that metric.

MediumTechnical

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Design a set of data quality checks a data engineer should run daily to ensure KPI accuracy for core product metrics (DAU, conversion rate, revenue). Include concrete checks, thresholds, and remediation steps.

HardTechnical

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You observe that conversions increased but revenue per user decreased. Propose a data-driven approach to determine whether this is due to a change in user mix, pricing, discounts, or product changes. List concrete SQL queries and analyses you would run.

HardSystem Design

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Design an automated alerting rule for a KPI that experiences natural weekly seasonality (e.g., DAU spikes on weekends). Explain why a static threshold is insufficient and propose a statistical rule (e.g., baseline + n sigma, seasonal decomposition) and how to parameterize it to balance timeliness and false positives.

MediumTechnical

0 practiced

Given an events table with event timestamps, design an SQL-based sessionization approach that groups user events into sessions using a 30-minute inactivity timeout. Then describe how you'd compute daily sessions per user and average session length for each day.

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