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.

MediumSystem Design

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A model inference pipeline is serving both synchronous user requests and periodic heavy analytics jobs. How would you architect resource isolation to prevent analytics jobs from impacting user-facing latency? Discuss containerization, queueing, autoscaling, and priority scheduling trade-offs.

EasyTechnical

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Compare batching and streaming inference approaches for a recommender model that receives variable request rates. Explain the latency and throughput trade-offs, how you would implement autoscaling differently for each, and when you'd choose one over the other.

HardTechnical

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Compare data-parallel and model-parallel distributed training strategies for a transformer model that does not fit in a single GPU's memory. Discuss the trade-offs in communication overhead, memory efficiency, ease of implementation, and convergence characteristics.

EasyTechnical

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You're given a Python inference microservice that is CPU-bound. Describe three low-effort profiling steps you would run to confirm the bottleneck and one small code change you might try that could give a noticeable improvement with minimal risk.

MediumTechnical

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You discover a single function accounts for 60% of request time in an inference service after profiling. Outline a step-by-step plan to refactor or optimize that function safely, including local benchmarking, incremental rollout, and rollback strategies.

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