Evaluate the candidates ability to solve complex multi layered technical and design problems by making reasonable assumptions, articulating trade offs, and handling edge cases. Candidates should show how to decompose problems that span networking caching persistence and performance optimization, select architectures and algorithms with explicit trade off analysis such as speed versus simplicity and functionality versus performance, and consider failure modes including network failures device limitations and concurrent access patterns. Strong responses include clear assumption statements, alternative approaches, complexity and cost considerations, testing and validation strategies, and plans to monitor and mitigate operational risks.
MediumSystem Design
0 practiced
Design a zero-downtime online model update flow for model serving. Explain how to load new weights, migrate or warm caches, handle in-flight requests, perform signature/compatibility checks, and guarantee no service interruption. Discuss patterns like blue-green, rolling updates, warm swaps, and canarying.
HardSystem Design
0 practiced
Design an architecture to serve personalized models on user-sensitive data across EU and non-EU regions while complying with data-locality and privacy regulations (e.g., GDPR). Discuss options: region-specific model training, federated learning, differential privacy, encrypted inference, and trade-offs between latency, accuracy, engineering complexity, and auditability.
HardSystem Design
0 practiced
Design an end-to-end architecture for a real-time multimodal AI assistant that handles text, speech (STT/TTS), and vision inputs. Requirements: sub-200ms p95 for text-only queries, safety filtering, user personalization, and per-region data residency. Detail component diagram, model routing, caching, microservices, edge vs cloud placement, and safety layers.
MediumSystem Design
0 practiced
Design a cost-optimized multi-tenant GPU inference platform that must host many small models with unpredictable, spiky traffic. Discuss model packing, container isolation, scheduling algorithms, fairness, tenant isolation, autoscaling, preemption policies, and how to measure utilization vs SLA risk.
HardTechnical
0 practiced
Propose a design to reduce inference latency by combining model splitting (early-exit layers), adaptive computation (skip layers dynamically), and caching of intermediate representations. Analyze accuracy vs latency trade-offs, training considerations, runtime decision logic, and monitoring needed to detect mispredictions introduced by early exits.
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