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Product / Decision Making 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 31–57 of 57 results

  • GoogleMediumProduct Manager
    YouTube Data Throughput Estimation

    Practice a YouTube daily data throughput Fermi estimate using daily users, watch time, average bitrate, overhead, unit conversions, sensitivity analysis, sanity checks, and a final exabytes-per-day range.

    Product / Decision MakingTechnical Screen
  • Lucid MotorsMediumProduct Manager
    Favorite Product Deep Dive & UX Focus

    Practice a favorite product and UX improvement interview case with a past UX-focused product story, jobs-to-be-done analysis, product positioning, key metrics, concrete improvement ideas, experiments, guardrails, and prioritization trade-offs.

    Product / Decision MakingOnsite
  • MetaMediumProduct Manager
    Children’s Bookshelf Product Design

    Practice a children's bookshelf product design case covering target users, caregiver and child pain points, safety principles, ergonomic design, MVP scope, intentional exclusions, validation methods, reading engagement metrics, and quality guardrails.

    Product / Decision MakingOnsite
  • MetaHardProduct Manager
    Google–Roomba Acquisition Strategy

    Practice a Google and Roomba acquisition strategy case with smart-home fit, user value, privacy governance, competitive landscape, financial modeling, regulatory risk, integration planning, alternatives, and a clear go or no-go recommendation.

    Product / Decision MakingOnsite
  • MetaMediumProduct Manager
    Hiking App: Design, Metrics, and Go-to-Market

    Practice designing a hiking mobile app with target users, value proposition, MVP features, roadmap, offline maps, safety trade-offs, metrics, instrumentation, launch strategy, partnerships, growth loops, and monetization options.

    Product / Decision MakingOnsite
  • MetaMediumProduct Manager
    Hyperlink Request Flow at Facebook

    Practice explaining the end-to-end hyperlink request flow in Facebook, covering mobile client events, routing, DNS, TLS, gateways, auth, GraphQL, caching, rendering, external safe-link handling, performance, security, privacy, and observability.

    Product / Decision MakingOnsite
  • MetaHardProduct Manager
    Meta PM Interview Questions

    Practice a mixed PM onsite loop with strategy, data, product design, architecture, analytics, technical leadership, and failure reflection prompts. The solution guide shows how to structure each case, ask clarifying questions, choose metrics, make trade-offs, and give practical recommendations.

    Product / Decision MakingOnsite
  • MetaMediumProduct Manager
    Meta Pay: Metrics & Prioritization

    Practice Meta Pay PM metrics and prioritization with North Star definition, metric tree, split-bill versus donations trade-offs, and debugging flat adoption despite lower cost per transaction. The solution covers active payers, transaction success, trust, risk, unit economics, feature strategy, and decomposition analysis.

    Product / Decision MakingOnsite
  • MetaMediumProduct Manager
    Program Execution Deep Dive

    Practice a Product Manager program execution deep dive covering requirements capture, prioritization, stakeholder conflict, risk mitigation, launch planning, staged rollout, metrics, guardrails, and post-launch learning.

    Product / Decision MakingOnsite
  • MicrosoftHardProduct Manager
    Enterprise Process Management Tool Design

    Practice an enterprise Business Process Management product design case covering user personas, jobs to be done, objectives, prioritization, MVP scope, workflow architecture, governance, integrations, compliance, observability, and success metrics.

    Product / Decision MakingOnsite
  • MicrosoftMediumTechnical Program Manager
    Evaluate an ML feature launch

    Prepare for the Evaluate an ML feature launch interview question with a structured prompt, clarifying questions, answer rubric, follow-up probes, and a stronger model solution. This product / decision making guide helps candidates frame assumptions, trade-offs, metrics, and role-relevant evidence without relying on generic answers.

    Product / Decision MakingHR Screen
  • MicrosoftMediumProduct Manager
    ML Pipeline Stability & Evaluation

    Practice diagnosing and stabilizing a time-series ML forecasting pipeline after online accuracy drops. The solution covers data ingestion, feature generation, training, serving, time-aware evaluation, segment metrics, baselines, online canaries, remediation, monitoring, and ML release governance.

    Product / Decision MakingHR Screen
  • MicrosoftMediumProduct Manager
    North Star Metrics & Experiment Design

    Practice defining a North Star Metric and experiment for a Microsoft product using Teams as the example. The solution defines Weekly Actively Collaborating Users, leading indicators with SQL sketches, telemetry requirements, randomization unit, experiment metrics, guardrails, power, and analysis.

    Product / Decision MakingHR Screen
  • NexonMediumProduct Manager
    Investigate Item Usage Spike

    Investigate an in-game item usage spike by verifying instrumentation, segmenting players and item sources, building hypotheses across patches, economy shifts, promotions, exploits, and bots, then choosing actions with retention, economy, monetization, and trust guardrails.

    Product / Decision MakingTechnical Screen
  • OracleEasySoftware Engineer
    Decide When to Simplify Scope and When to Prioritize a Bug Fix

    This question evaluates your framework for prioritizing engineering work when faced with competing demands: feature scope reduction versus choosing between bug fixes and new feature development.

    Product / Decision MakingOnsitePrioritizationProduct ManagementDecision Making
  • SplunkMediumProduct Manager
    Pricing Splunk’s New Service

    Practice a Splunk B2B pricing case covering customer segmentation, value-based pricing, cost-plus floors, competitive benchmarking, packaging tiers, value metrics, add-ons, pricing research, enterprise experiments, discounting, and post-launch monitoring.

    Product / Decision MakingOnsite
  • TikTokMediumProduct Manager
    Break a Palindrome Algorithm

    Practice the greedy algorithm for breaking a palindrome into the lexicographically smallest non-palindrome by one character replacement. The solution explains scanning the left half, skipping the middle character, changing the first non-a to a, handling all-a cases, proving correctness, and analyzing complexity.

    Product / Decision MakingOnline Assessment
  • TikTokMediumProduct Manager
    Digital Marketing Campaign ROI Analysis

    Practice allocating budget between two TikTok mobile-game ad creatives using funnel and ROI analysis. The solution covers CTR, CVR, IPM, CPI, ARPU, ROAS, marginal return, creative fatigue, growth headroom, long-term LTV, confidence, and exploration-versus-exploitation decisions.

    Product / Decision MakingOnsite
  • TikTokMediumProduct Manager
    Login Flow Critique, Feature Improvement & Data Privacy

    Practice a TikTok product case covering login-flow critique, feature improvement, and data safeguards. The solution evaluates pre-login value, auth friction, OTP and account recovery issues, proposes Smart Collections, defines experiment metrics, and outlines privacy, security, and data-integrity controls.

    Product / Decision MakingTechnical Screen
  • TikTokHardProduct Manager
    TikTok Product Strategy & Case Design

    Practice TikTok PM product decision-making across feature selection, prioritization, Stories strategy, and VR app store design. The solution covers strategy, user problems, RICE-style prioritization, TikTok-native ephemeral content trade-offs, MVP metrics, and immersive VR discovery, safety, and developer tooling.

    Product / Decision MakingOther
  • TinderHardProduct Manager
    Profile Customization for Millennials & Gen Z

    Practice designing a Tinder profile-customization feature for Millennials and Gen Z without slowing the swipe experience. The solution proposes modular Profile Layers, explains cohort needs, safety and moderation constraints, research validation, launch sequencing, and metrics for match quality, conversations, retention, and guardrails.

    Product / Decision MakingOnsite
  • UberMediumTechnical Program Manager
    Fix unclear delivery addresses

    Diagnose and fix unclear delivery addresses in a marketplace delivery product. Covers root-cause analysis, risky-address detection, address validation, courier recovery, metrics, guardrails, and phased rollout.

    Product / Decision MakingTechnical Screen
  • VisaMediumProduct Manager
    Product Roadmap & Stakeholder Trade-offs

    Practice explaining an end-to-end product roadmap process with stakeholder trade-offs. The solution covers outcome-driven planning, input collection, prioritization frameworks, alignment rituals, Now/Next/Later planning, and a concrete example balancing sales, risk, engineering, and customer needs.

    Product / Decision MakingTechnical Screen
  • WayfairMediumApplied Scientist
    Evaluate Post-Purchase Recommendation Emails

    Evaluate post-purchase recommendation emails through incremental profit, useful timing, customer eligibility, randomized holdouts, and unsubscribe or complaint guardrails.

    Product / Decision MakingOnsite
  • ZomatoMediumProduct Manager
    Selecting 500 of 1,000 U.S. Restaurants to Onboard Without Comparable Data

    An execution question set in 2014: a team three weeks behind schedule must choose 500 of 1,000 U.S. restaurants to onboard with no comparable metrics or market data. It tests goal setting, prioritization when data is missing, delivery under a schedule slip, and using early results to guide later choices.

    Product / Decision MakingOther
  • eBayMediumProduct Manager
    How would you prioritize and test features?

    Explain how to prioritize and test eBay marketplace features using a first-time seller example. The solution covers goal framing, RICE prioritization, MVP execution, A/B testing, seller activation metrics, buyer trust guardrails, and marketplace tradeoffs.

    Product / Decision MakingOnsite
  • xAIMediumFrontend Engineer
    Plan a Project Across Customers and Partner Teams

    Plan a cross-team customer project through outcome clarification, dependency ownership, milestone evidence, uncertainty, scope tradeoffs, and clear stakeholder communication.

    Product / Decision MakingOnsite