Product Metrics and Health Questions

Designing and using product specific metrics to measure user experience product health and business impact. Topics include identifying a north star metric and supporting metrics at company product and feature levels, measuring activation adoption engagement retention daily active users and monthly active users feature adoption rates and time to value, using product telemetry experimentation and funnel analysis to measure feature impact, and connecting product metrics to monetization and strategic objectives. Candidates should be able to propose metrics for new features justify trade offs instrument tracking and explain how product metrics inform prioritization roadmap and stakeholder alignment.

HardTechnical

89 practiced

Discuss known biases and pitfalls when relying on DAU/MAU as a primary engagement metric. Propose three alternative or complementary sticky metrics appropriate across product types and explain how you would present these alternatives to executives who are used to DAU/MAU.

EasyTechnical

91 practiced

As a BI Analyst for a subscription video service, explain the difference between 'acquisition' and 'activation'. Define one measurable activation metric for users who start a free trial (include event names and precise criteria) and justify why it is a good activation signal.

MediumTechnical

79 practiced

List and explain the data-quality checks you would implement on product telemetry pipelines to ensure reporting accuracy. Include checks for schema changes, event volume monitoring, uniqueness constraints, null rates, and SLA monitoring for delayed ingestion.

EasyTechnical

80 practiced

You track a 4-step funnel: Sign-up → Email Confirm → First-Project Created → First-Paid Conversion. In the last 30 days counts were: Sign-ups=10,000; Email Confirms=7,500; First-Project=3,250; First-Paid=325. Compute step conversion rates and the overall conversion. Identify the biggest bottleneck and suggest one experiment to address it.

MediumTechnical

120 practiced

You have a BigQuery events table:

events(event_id STRING, user_id STRING, event_name STRING, event_timestamp TIMESTAMP)

Write a SQL query (BigQuery dialect) to compute for each date in the last 30 days: daily DAU, trailing-30-day MAU, and DAU/MAU ratio. Explain any assumptions about timezone and unique user counting.

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