Metric Definition and Implementation Questions

End to end topic covering the precise definition, computation, transformation, implementation, validation, documentation, and monitoring of business metrics. Candidates should demonstrate how to translate business requirements into reproducible metric definitions and formulas, choose aggregation methods and time windows, set filtering and deduplication rules, convert event level data to user level metrics, and compute cohorts, retention, attribution, and incremental impact. The work includes data transformation skills such as normalizing and formatting date and identifier fields, handling null values and edge cases, creating calculated fields and measures, combining and grouping tables at appropriate levels, and choosing between percentages and absolute numbers. Implementation details include writing reliable structured query language code or scripts, selecting instrumentation and data sources, considering aggregation strategy, sampling and margin of error, and ensuring pipelines produce reproducible results. Validation and quality practices include spot checks, comparison to known totals, automated tests, monitoring and alerting, naming conventions and versioning, and clear documentation so all calculations are auditable and maintainable.

MediumTechnical

0 practiced

Explain how to compute a 28-day rolling retention metric and how it differs from cohort retention. Provide the formula and describe how to compute it efficiently across large datasets in SQL or an analytical engine.

HardTechnical

0 practiced

A metric derived table is costing too much to recompute nightly. Propose optimization strategies: materialized views, incremental updates, partition pruning, pre-aggregation, or change data capture. For each option explain complexity, cost trade-offs, and when to choose it.

HardSystem Design

0 practiced

You are combining data from a high-throughput event stream and a nightly batch financial system for revenue metrics. Discuss trade-offs between computing metrics in streaming vs batch, where to join data (stream-side, batch-side, or in a serving layer), and how to keep results reproducible.

EasyTechnical

0 practiced

Describe a practical approach to decide the time window (daily, weekly, monthly) for a metric like 'Active Paying Users' in a product used globally across time zones. Explain trade-offs between sensitivity and stability, and how business cadence affects your choice.

HardTechnical

0 practiced

Design an automated test suite for a metric pipeline that computes daily MAU: include at least 5 types of tests you would implement (e.g., regression, sanity, distribution, null checks). For each test type explain what triggers it and what threshold would raise an alert.

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