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Analytics & Experimentation Interview Questions

Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.

9403
Questions
546
Companies
11
Topics
8
Languages

Showing 1–6 of 6 results

  • AmazonMediumProduct Manager
    Experiment Design to Measure Whether Voice Ordering Lifts Amazon Purchase Frequency

    An Amazon product manager prompt asking how to design an A/B test for a new voice-shopping feature and learn whether it makes customers buy more often. It tests the choice of randomization unit, how purchase frequency is measured, sample sizing for noisy order counts, and guardrails such as accidental orders and order value.

    Analytics & ExperimentationOther
  • MetaMediumProduct Manager
    Goals, North-Star Metric and Guardrails for Scam Protection Across Meta's Apps

    A metrics question asking a product manager to set goals and success metrics for a scam-prevention product across Meta's apps. It tests goal prioritization, the choice and measurement of a north-star metric, leading indicators by scam type and surface, guardrails against over-enforcement, and measurement under adversarial behavior.

    Analytics & ExperimentationOther
  • MetaMediumProduct Manager
    North-star and guardrail metrics for generative AI across Meta's apps

    A product manager metrics question on judging whether Meta's generative AI features, such as its in-app assistant, creation tools and ad tools, are succeeding. It tests choosing a north-star metric that reflects real value, supporting and guardrail metrics, and how to show the AI actually caused any change.

    Analytics & ExperimentationOther
  • MetaMediumProduct Manager
    Root-Causing an Instagram Satisfaction Decline During Ad Monetization Growth

    A Meta product manager interview question about Instagram, where ad revenue has risen while user satisfaction has fallen. It tests validating both metrics, decomposing revenue and satisfaction to find a shared cause, testing hypotheses with holdouts, and weighing short-term revenue against long-term user health.

    Analytics & ExperimentationOther
  • NetflixHardData Scientist
    Design A/B Test for Streaming Feature Network Effects

    This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design A/B Test for Streaming Feature Network Effects states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Analytics & ExperimentationOther
  • SwiggyMediumProduct Manager
    Root-Causing Slower Instamart Deliveries in a Tier-1 City Since Last Week

    A product manager metric-diagnosis question about Swiggy Instamart orders in a Tier-1 city taking 10% longer than the expected standard delivery time since last week, across every order type. It tests metric validation, breaking delivery into stages, segmentation, and separating internal from external causes.

    Analytics & ExperimentationOther