A strong answer checks data freshness and pipeline timing first, then confirms the definition of "active" hasn't changed, before assuming a real product problem.

7. Two dashboards show different numbers for the same metric. How do you figure out which one, if either, is right?

Look for: checking the grain of the underlying tables, the definition used for the metric, the join logic, and the data freshness, rather than assuming one is simply correct.

8. How would you measure whether a new onboarding flow improved activation?

Tests whether the candidate defines activation precisely, picks a fair comparison group and time window, and considers confounding factors, not just "compare before and after."

9. When would you choose to write a query yourself versus asking a data analyst for help?

A strong answer reasons about permanence and blast radius, as in Lesson 4.4, not just personal SQL confidence.

10. How do you decide what counts as "churned" for a subscription product?

Look for recognition that this is a product definition with real tradeoffs (voluntary vs. involuntary churn, grace periods, downgrades vs. full cancellation), not a purely technical question.

SQL for Product Managers

Module 1Why SQL Is a PM Skill

Module 2Core SQL Every PM Must Know

Module 3Building Real Product Analysis

Module 4Working With Data Like a PM, Not an Analyst

Module 5Advanced Concepts and Working Cross-Functionally

Module 6Write and Run a Real Product Metrics Query Set

Module 725 SQL and Data Interview Questions for Product Managers

Product Judgment