Distributed Systems Fundamentals Questions

Core principles and theory that underlie distributed computing systems. Includes understanding trade offs between consistency, availability, and partition tolerance, common consistency models such as eventual and strong consistency, replication and sharding strategies, load balancing and data partitioning, consensus algorithms and their guarantees, scalability and fault tolerance patterns, and how these concepts apply to infrastructure components such as databases, caches, service meshes, and load balancers. Candidates are expected to explain design choices, common failure modes, and how fundamental concepts influence architecture decisions for resilient and scalable systems.

MediumTechnical

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Design a canary deployment plan for a new model version across a distributed serving cluster. Include traffic splitting strategy, monitoring metrics (latency, error rate, model-quality metrics), statistical tests to decide promotion, rollback triggers, and how you would handle delayed signals such as conversions.

HardSystem Design

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Design a distributed serving architecture for an ensemble of models where each request may be routed to a fast lightweight model or a heavyweight accurate model. Address routing policy, caching of expensive inferences, asynchronous scoring, fallback strategies when heavy models are slow or unavailable, and how to A/B test ensemble configurations.

MediumTechnical

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Compare synchronous and asynchronous gradient update schemes in distributed training. Explain the staleness problem for asynchronous updates, its impact on convergence, and mitigation techniques like bounded staleness, adaptive learning rates, and gradient correction approaches.

MediumTechnical

0 practiced

Explain weaker consistency models: read-your-writes, monotonic reads, monotonic writes, and causal consistency. For each model, describe an example in an AI-serving context (e.g., cross-device personalization) and practical techniques to implement them.

HardBehavioral

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Describe a production incident you led where a model's predictions caused significant user impact. Explain how you coordinated diagnosis and mitigation across teams, how you communicated with stakeholders and users, what technical steps you took to restore service, and what preventive measures you instituted afterwards.

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