Easy interview questions in Analytics & Metrics, page 4
Describe client-side events and properties to track Export to CSV usage
This tests telemetry design for async actions. A strong answer defines three custom events—click, success, failure—with properties like location, file_size, error_code, and user_id, fired at the right lifecycle moments.
Track an 'Export to CSV' button's usage
This tests modeling a feature's full lifecycle (intent, success, failure) in analytics. Propose one custom event like export_interaction with a status parameter ('success', 'failure') fired on completion, not just on click.
Track an 'Export to CSV' button's usage and outcomes
This tests your ability to design a robust event schema, not just track a click. A great answer uses one custom event name with a 'status' parameter ('initiated', 'success', 'failure'). A red flag is suggesting multiple event names for one action.
What data do you need and what steps build a WAU dashboard?
Tests defining a metric, modeling events, and wiring them into a BI tool. A strong answer names the feature event, sets a rolling 7-day window, counts distinct users by period, and configures the BI layer. Red flag: jumping to charts before defining active.
How would you build a weekly active user dashboard?
This tests translating a business need into a technical spec. First, clarify the definition of "active." Then, identify the event data needed (user_id, timestamp). Finally, outline the ETL and BI tool steps. A red flag is not clarifying the metric first.
How would you build a weekly active user dashboard?
This tests your ability to translate a business request into a technical plan. A good answer defines "active," identifies necessary data (user ID, timestamp, event), outlines the data modeling, and explains the BI tool implementation.
Instrument a mobile event and surface it in analytics
This tests full-stack analytics plumbing. A good answer hits: structured client logging, batched transmission, backend validation, warehouse aggregation, and dashboard verification. A red flag is fire-and-forget logging with no schema checks or reconciliation.
Instrumenting a New User Interaction in a Mobile App
Tests your understanding of the end-to-end analytics pipeline. A good answer defines the event, instruments it on the client with an SDK, verifies it in a debug view, and confirms its arrival in the data warehouse after the ETL process.
Instrumenting a New User Interaction for Analytics
Tests your grasp of the full data lifecycle. A good answer covers event definition, client-side implementation, the backend pipeline, and end-to-end verification.
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