CAP Theorem and Consistency Models Questions

Understand the CAP theorem and how Consistency, Availability, and Partition Tolerance interact in distributed systems. Know different consistency models including strong consistency such as linearizability, eventual consistency, causal consistency, and session consistency, and how to apply them to different use cases. Be familiar with consensus protocols and distributed coordination primitives such as Raft and Paxos, quorum reads and writes, two phase commit and when to use them. Understand trade offs between consistency and availability under network partitions, patterns for hybrid approaches where different data uses different guarantees, and the product and developer experience implications such as latency, stale reads, and API contract clarity.

HardSystem Design

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Design an explicit API specification for prediction endpoints that transparently expose consistency guarantees and staleness metadata. Provide example response fields or headers (for example: model-version, last-updated-timestamp, consistency-level, staleness-ms) and describe client behavior for each consistency level and error code.

MediumTechnical

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Design fallback strategies for serving predictions during a network partition or partial outage. Consider options such as serving cached model outputs, using an older model version, computing features locally, or degrading to a simpler rule-based model. Discuss trade-offs in terms of user experience, correctness, and detectability.

EasyTechnical

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Explain the CAP theorem and describe how an ML model serving system might trade consistency, availability, and partition tolerance when choosing architecture. Give concrete examples of when an ML system should prioritize availability over consistency and when it should prioritize consistency over availability, and mention the product impacts of each choice.

EasyTechnical

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How does eventual consistency complicate A/B testing and online experiments for ML models? Describe precautions and experimental design changes needed to ensure that observed metric differences reflect model changes and not temporary inconsistency effects.

HardSystem Design

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Design a model serving system for 100 million users globally with the specific requirement that account-balance predictions must be linearizable while recommendation predictions may be eventual. Provide architecture, data partitioning, replication, routing logic, and client-side considerations to safely combine these guarantees in a single platform.

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