Solution Approach & Modeling Strategy Questions
Techniques for approaching system design problems and architectural modeling in distributed systems, including problem framing, requirement elicitation, modeling abstractions (data flows, component boundaries, API interactions), trade-off analysis, and evaluation criteria for scalability, reliability, and maintainability.
HardSystem Design
0 practiced
Architect a retrieval-augmented-generation (RAG) inference pipeline capable of serving 10k QPS for a QA product with a median end-to-end latency target of 200ms. Include components such as request ingress, prompt construction, vector index lookup (shards/replicas), passage caching, reranker, generator model hosting, batching, cache warmers, and circuit-breakers. Explain sharding, routing, and hotspot mitigation strategies for the vector index and how to measure user-perceived quality.
MediumSystem Design
0 practiced
Discuss architectural trade-offs between a centralized feature store service and pushing feature computation into per-service feature-serving components. Consider latency, reuse across teams, consistency guarantees for online inference, ownership boundaries, debugging complexity, and reproducibility for offline training.
MediumTechnical
0 practiced
For a personalization service that consumes streaming user events and serves online inference across many replicas, describe how you'd model and guarantee feature freshness. Discuss eventual consistency vs strong consistency trade-offs, staleness windows, version tagging of features, and mechanisms (e.g., monotonic timestamps, causal metadata, vector clocks) to bound inconsistencies across replicas.
MediumSystem Design
0 practiced
Design a multi-region inference architecture for a conversational AI that must provide <100ms median latency globally, tolerate a single-region outage, and respect regional data residency requirements. Describe where models should be placed, how to handle state replication and caches, traffic routing (geo-DNS, anycast, edge), and how to perform safe failover and model rollout across regions.
MediumSystem Design
0 practiced
Design an inference platform to support 50k QPS of small NLP models (~100MB) with a 99th-percentile latency SLO of 50ms. Describe resource allocation (CPU vs GPU), autoscaling strategy, request batching, request routing, warm pool management, cold start mitigation, and cost controls. Provide a component diagram showing request flow from ingress through model instances and cache.
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