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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.

HardSystem Design
0 practiced
Design a scalable ETL architecture to compute daily KPIs (DAU, new-users, revenue) for a product with 100M users and trillions of events per month. Outline major components (ingest, storage, compute), technology choices, cost/performance trade-offs, and how you'd ensure reproducibility and correctness of KPI computations.
HardTechnical
0 practiced
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
0 practiced
You're designing event instrumentation to support reliable measurement of DAU, sessions, and conversions for both web and mobile. Propose an event schema (fields and types) and explain how you would enforce schema changes and backward compatibility in production analytics pipelines.
MediumTechnical
0 practiced
Decompose the business outcome 'increase ARPU (average revenue per user) by 15% in 6 months' into component metrics that are actionable for engineering and product teams. For each component metric, state how a data engineer would measure it and what an associated short-term action could be.
MediumTechnical
0 practiced
Describe steps to detect whether a sudden spike in a metric is caused by an instrumentation bug. Provide a systematic checklist of queries and pipeline checks, and explain what evidence would convince you it is an instrumentation issue rather than real user behavior.

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