OKR and Metric Definition Questions

Covers translating strategic objectives into measurable key results and operational metrics that drive the right behaviors. Topics include writing clear objectives and specific, measurable key results, distinguishing leading and lagging indicators, defining primary metrics versus guardrail metrics, selecting absolute versus relative targets, avoiding perverse incentives, and ensuring metric hygiene through reliable instrumentation and data quality checks. Also addresses setting appropriate targets and time horizons, monitoring cadence, dashboard design and alerting for metric deviations, and using metrics to inform prioritization and continuous improvement without encouraging gaming.

HardSystem Design

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Design an automated alerting system for metric deviations that minimizes false positives while still surfacing meaningful issues. Describe detection algorithms (statistical and heuristic), thresholding strategy, how to combine multiple signals, noise-reduction techniques, and an escalation workflow for alerted events.

MediumTechnical

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Describe a practical method to set stretch versus achievable targets for a KR using historical variability and probabilistic modeling. Include what historical window you would choose, how you would estimate uncertainty, and how you would express targets to stakeholders.

EasyTechnical

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A chat feature is launching. List three primary metrics you would track to measure success and two guardrail metrics to ensure quality and safety. For each metric, provide a one-sentence rationale and specify the numerator and denominator where appropriate.

MediumTechnical

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Write a SQL query to compute cohort retention rates for users who signed up in each week and their percent of users who returned at day 7 and day 30. Schema: users(user_id, signup_date date), events(user_id, event_date date, event_type). Show cohort_week_start, cohort_size, day_7_retention, day_30_retention. Explain assumptions about users with multiple events.

MediumTechnical

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You wake up to an alert: one key metric dropped 18% overnight. Outline your triage checklist as a Data Analyst to determine whether the issue is instrumentation, ETL/data pipeline, a product regression, or a real business change. Provide proposed quick checks you would run and the expected evidence each check would produce.

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