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.

HardTechnical

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Design an automated system to detect and compensate for training-serving skew caused by eventual consistency of feature updates. Specify metrics to compute skew, alerting thresholds, automated rollback triggers, and a remediation pipeline that can re-train or re-serve models as needed.

MediumTechnical

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Compare synchronous SGD, asynchronous SGD, and bounded staleness approaches for distributed training. Evaluate them in terms of convergence speed, communication cost, resilience to stragglers and partitions, and how they map to consistency models discussed by CAP.

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.

EasyTechnical

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When using caches for model inference at the edge, CDN, or local node, what consistency issues can arise that affect prediction correctness? Propose practical cache invalidation strategies and explain how each strategy impacts latency and risk of stale predictions.

EasyTechnical

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Describe partition tolerance in distributed systems and give at least three realistic examples of network partition scenarios that could affect cloud-hosted model serving infrastructure. For each example, briefly state how an ML service should be prepared to behave.

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