Optimization and Technical Trade Offs Questions

Focuses on evaluating and improving solutions with attention to trade offs between performance, resource usage, simplicity, and reliability. Topics include analyzing time complexity and space complexity, choosing algorithms and data structures with appropriate trade offs, profiling and measuring real bottlenecks, deciding when micro optimizations are worthwhile versus algorithmic changes, and explaining why a less optimal brute force approach may be acceptable in certain contexts. Also cover maintainability versus performance, concurrency and latency trade offs, and cost implications of optimization decisions. Candidates should justify choices with empirical evidence and consider incremental and safe optimization strategies.

HardTechnical

47 practiced

Discuss how you would reason about cost vs performance when choosing to serve an ML model on on-demand cloud GPU instances versus maintaining an on-prem GPU cluster. Cover utilization patterns, operational overhead, latency, and capital expenditures.

HardSystem Design

49 practiced

You are designing a model caching strategy across microservices where models are large and updated frequently. Propose a deployment pattern to reduce update propagation latency while minimizing memory overhead (e.g., shared memory, memory-mapped files, lazy-loading). Discuss trade-offs and safety concerns.

MediumSystem Design

47 practiced

Design an inference-serving architecture for a classification model that must sustain 10k QPS with 95th percentile latency under 100ms. Include components (load-balancer, model servers, autoscaling, caching), how you would handle model warming, and any trade-offs in your design.

HardSystem Design

56 practiced

Design an online feature computation system that supports per-user sliding-window aggregations with 10ms query latency and 100k updates/sec ingestion. Discuss state storage choices, approximate algorithms (e.g., sketches), window sizes, and trade-offs in consistency and resource usage.

EasyTechnical

73 practiced

Implement an LRU cache in Python with O(1) get and put operations. Provide a class with methods: get(key) -> value or -1, and put(key, value). The cache must support a maximum capacity and evict the least recently used item when full. Use standard Python (no external libs).

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