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

58 practiced

Design an experiment to measure the impact of partial consistency (stale reads) on model performance in production. Define metrics to collect, dataset selection, how to inject or simulate staleness, sample sizing for statistical significance, and safe rollback criteria if the experiment shows degradation.

MediumSystem Design

57 practiced

Design a scalable model inference service that can handle 10,000 requests per second with p95 latency under 50ms for small transformer-based models. Include service components, autoscaling strategy, load balancing, caching, batching, model warm-up, GPU vs CPU placement, monitoring, and key failure modes to guard against.

MediumTechnical

59 practiced

Design an approach to ensure idempotency of model inference requests in a distributed setting where clients may retry. Discuss idempotency keys, deduplication windows, dedup caches, handling side-effects (logging, billing), and trade-offs between strict deduplication and resource overhead.

MediumTechnical

68 practiced

Explain consensus algorithms Paxos and Raft at a high level and why Raft is considered easier to understand and implement. How would you use a consensus protocol to elect a leader for coordinating tasks like checkpoint orchestration or parameter updates in distributed training?

HardTechnical

69 practiced

Your distributed inference pipeline experiences cascading failures: a downstream feature-service becomes slow and causes upstream request buildup and timeouts. Describe how to apply backpressure, design timeouts and retry logic, use bulkheads and circuit breakers, and architect graceful degradation paths (e.g., fallback to cached or simpler models).

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