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.
MediumTechnical
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Compare A/B testing and multi-armed bandit approaches for pricing experiments. For a dynamic environment where demand changes hourly, which approach would you choose and why? Discuss bias, sample complexity, and long-term rewards.
HardSystem Design
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You must design a pricing service that can sustain 1M QPS at peak with p95 latency under 10ms for cached lookups. Propose an architecture covering partitioning/sharding, multi-tier cache (edge/regional/central), autoscaling policies, and techniques to ensure predictable tail latencies at scale.
HardSystem Design
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Discuss the pros and cons of moving parts of the pricing decision to the mobile edge or local edge nodes: lower latency vs. trust/anti-fraud concerns, model update complexity, offline capability, and security. Propose an architecture that supports safe edge pricing with periodic reconciliation.
MediumTechnical
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Write high-level Python pseudo-code for a scoring API handler that: 1) reads request inputs, 2) fetches online features from a cache/feature-store, 3) falls back to streaming aggregator if cache miss, 4) calls a TensorFlow SavedModel for inference with batching support, and 5) returns a JSON price response. Describe thread-safety and latency considerations in comments.
MediumTechnical
0 practiced
For a global platform, explain the trade-offs between eventual consistency and strong consistency for serving prices across regions. Propose a consistency model for prices and a synchronization mechanism that minimizes user-visible inconsistencies while keeping latency low for local requests.
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