Surge Pricing and Dynamic Pricing System Design Questions
Design considerations for building a scalable, low-latency surge pricing engine and dynamic pricing system within a distributed architecture. Covers data modeling for pricing rules, real-time computation, demand/supply signal integration, multi-region consistency, latency and throughput requirements, caching and cache invalidation strategies, event-driven and microservices approaches, fault tolerance, data synchronization with inventory and orders, feature flags and A/B testing, deployment strategies, monitoring, and reliability concerns.
HardTechnical
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Propose a reinforcement-learning approach for dynamic surge pricing that maximizes long-term revenue while maintaining fairness and driver incentives. Describe state representation, actions, reward shaping, safe exploration constraints, offline policy evaluation, and how you'd validate in a simulator before production.
HardTechnical
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Design an experiment (incl. assignment, metrics and statistical tests) to measure spillover effects: does raising prices in a region cause drivers to migrate to neighboring regions and consequently affect their pricing? Explain how to measure displacement and the statistical controls you would use.
MediumTechnical
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You are tasked with lowering the cost of real-time model inference without violating latency SLOs. Propose concrete approaches (model distillation, quantization, batching, caching, tiered serving) and discuss trade-offs and which you'd prioritize for a surge-pricing model.
MediumTechnical
0 practiced
If the ML model or primary pricing service fails, what fallback strategies would you implement to ensure users still get acceptable prices? Compare returning a cached price, using a rule-based default, or denying the request. Discuss pros/cons and how you would test fallback correctness.
HardTechnical
0 practiced
Attackers may try to game surge by simulating false demand or colluding drivers. Design detection and mitigation strategies across data-validation, anomaly detection, rate-limiting, and incentives. Include how to incorporate such defenses into the pricing pipeline to avoid false positives harming real users.
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