Metric Frameworks and Goal Alignment Questions

Understand how to choose, define, and apply metric frameworks that align product work to company objectives. Topics include common frameworks such as Acquisition, Activation, Retention, Revenue, Referral as well as selecting a single North Star metric that represents overall business success. Candidates should be able to define metrics at multiple levels including feature level, product level, and business level; distinguish leading indicators from lagging indicators and explain how leading metrics predict lagging outcomes; decompose a North Star into measurable submetrics and team level signals that teams can influence directly; set measurable targets and success criteria; and explain why a given metric is the most appropriate North Star for a particular business model. Practice scenarios include choosing metrics for feature launches, improving conversion or retention, reducing friction in checkout flows, and increasing engagement or virality, and describing how those metrics map to business outcomes and Objectives and Key Results.

MediumTechnical

24 practiced

Given users(user_id bigint, signup_at timestamp) and transactions(transaction_id bigint, user_id bigint, amount numeric, occurred_at timestamp), write a SQL query to compute cohort LTV for users grouped by signup week over a 12-month horizon. Include handling of refunds (negative amounts) and explain your approach to extrapolate LTV beyond observed data with justification.

MediumTechnical

27 practiced

A company with multiple products asks you to maintain a single North Star while ensuring each product team has measurable signals they can influence. Propose a framework for decomposing the North Star into product-level signals, define ownership boundaries, and explain how to avoid double-counting users or actions across products.

MediumTechnical

27 practiced

You need to run an A/B test to increase onboarding activation. Baseline activation is 20% and you expect a 10% relative uplift (to 22%). Describe how you would: (1) choose a primary metric and guardrails, (2) compute the required sample size per variant for 80% power at alpha=0.05 including formula and assumptions, and (3) adjust calculations for multiple segments or expected variance heterogeneity.

HardSystem Design

25 practiced

Design an experiment to measure the effect of a viral sharing feature in a social network where users influence one another (violating SUTVA). Explain why a traditional randomized A/B design is invalid, propose alternative designs such as cluster randomization or stepped-wedge, describe analysis approaches to account for interference, and discuss power considerations and sample size implications.

MediumTechnical

35 practiced

You have several team-level signals such as weekly-active-users, average-sessions-per-user, and conversion-rate and need to build a composite health score for the product. Describe how you would construct this composite metric including normalization methods, weighting schemes, smoothing, stability concerns, interpretability, and how you would validate that this metric is a useful proxy for business success.

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