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.

HardSystem Design

74 practiced

For a streaming online feature store, propose a backup and disaster-recovery plan that covers: corrupt state, region failure, and accidental deletion. Include RPO/RTO targets, snapshot cadence, and how to verify recovery correctness for ML models in production.

HardSystem Design

83 practiced

For a distributed ML inference pipeline composed of several microservices, propose an observability plan that captures traces, metrics, and logs. Describe how to correlate a single prediction request across services, detect regression in model quality vs infrastructure issues, and create alerting thresholds.

HardSystem Design

75 practiced

Design a distributed rate limiter for a global inference API that enforces both per-user quotas (e.g., 100 requests/minute) and a global burst budget. The system must provide strong guarantees during partitions and be reasonably low latency. Describe algorithms, state distribution, and how you handle partitions.

EasyTechnical

82 practiced

What is the circuit breaker pattern and how would you apply it to an ML inference pipeline to prevent cascading failures when a downstream feature store or model-scoring microservice becomes unhealthy?

HardSystem Design

79 practiced

Design a distributed parameter server optimized for sparse recommendation model training where parameter updates are sparse and embeddings are large. Describe sharding, caching, network patterns, and how you would provide fault tolerance with minimal overhead.

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