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.

EasyTechnical

0 practiced

Write a precise metric definition for 'Daily Active User' (DAU) and 'Monthly Active User' (MAU') for a cross-device social app. Include: event(s) that count as activity, deduplication rules (per-user, per-device), timezone considerations, session timeouts, and how you handle users with multiple accounts.

HardTechnical

0 practiced

Compare the trade-offs between storing metrics at the user-level (pre-aggregated per user) versus event-level (raw events) for analytics and KPI computation. Discuss storage cost, query flexibility, recomputation cost, and latency, and recommend a hybrid strategy for a medium-sized company.

MediumTechnical

0 practiced

Write an SQL query to compute a 7-day rolling average of daily DAU given an events table (events(user_id, occurred_at)). The output should be: date, dau, dau_7day_avg. Use ANSI SQL and ensure deduplication per user per day.

MediumTechnical

0 practiced

You must choose between computing hourly KPIs via real-time streaming (higher cost, low latency) versus nightly batch jobs (lower cost, higher latency). Describe the decision framework a data engineer should use including business impact, data freshness requirements, cost, operational complexity, and fallbacks.

HardSystem Design

0 practiced

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.

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