Data Driven Decision Making Questions

Using metrics and analytics to inform operational and strategic decisions. Topics include defining and interpreting operational measures such as throughput cycle time error rates resource utilization cost per unit quality measures and on time delivery, as well as growth and lifecycle metrics across acquisition activation retention and revenue. Emphasis is on building audience segmented dashboards and reports presenting insights to influence stakeholders diagnosing problems through variance analysis and performance analytics identifying bottlenecks measuring campaign effectiveness and guiding resource allocation and investment decisions. Also covers how metric expectations change with seniority and how to shape organizational metric strategy and scorecards to drive accountability.

EasyTechnical

0 practiced

Using the schemas users(user_id, signup_date) and events(event_id, user_id, event_date, event_type), write an ANSI SQL query or describe the steps to compute Day-7 retention: percentage of users per signup cohort who performed any event on day 7 after signup. State assumptions about timezones and late-arriving events.

HardTechnical

0 practiced

Explain the statistical problem of 'peeking' in experiments and why continuous checks inflate false positive rates. Describe valid sequential testing approaches (for example: alpha-spending functions, group sequential tests, and Bayesian sequential methods) and recommend how to implement stopping rules and guardrails in an experimentation platform used by many product teams.

MediumTechnical

0 practiced

Given tables orders(order_id, user_id, order_date, amount) and returns(return_id, order_id, return_date, amount_refunded), write an ANSI SQL query to compute net revenue per calendar month and month-over-month percentage change, making sure to correctly account for refunds that occur in a different month than the original order.

MediumTechnical

0 practiced

Explain correlation versus causation using a product example where naive correlation could mislead decisions (e.g., ads correlate with retention). Describe two practical methods (besides randomized experiments) you could use to strengthen causal claims in product analytics and when each is appropriate.

HardSystem Design

0 practiced

Discuss the pros and cons of a centralized experimentation platform (one platform for the whole company) versus decentralized team-level experimentation (each team manages tests). Address concerns about velocity, statistical validity, data lineage, consistency, and operational risk. As a PM for a fast-growing company, recommend a model and outline a transition plan.

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