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Core
CAP and Consistency Models
The CAP theorem says that during a network partition a distributed system must choose between consistency and availability; you cannot have both while the network is split. PACELC extends it: even when there is no partition, you trade latency against consistency. Consistency models form a spectrum from linearizability (acts like one copy, real-time order) down through causal to eventual consistency. Logical clocks (Lamport, vector) order events without synchronized wall clocks. Applied-AI interviews probe it because every replicated store, queue, and feature pipeline lives somewhere on this spectrum, and naming the point precisely separates senior candidates.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
System Design for AI in ProductionDesign a system that records events in a single globally consistent order across many machines.→System Design for AI in ProductionHow do CAP and consistency tradeoffs apply to an ML feature store and online serving?→System Design for AI in ProductionDesign a distributed key-value store (partitioning, replication, and consistency).→System Design for AI in ProductionDesign a payment ledger that records money movement with exactly-once semantics and no lost cents.→System Design for AI in ProductionDesign a distributed cache like Redis or Memcached that serves millions of reads per second.→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→
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