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.

HardSystem Design

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Design a monitoring and alerting plan to detect performance regressions after deploying a model optimization that reduces latency but changes internal quantization. Specify key metrics, alert thresholds, canary evaluation duration, and automated rollback criteria.

HardTechnical

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Given a naive O(n^2) implementation of computing pairwise similarity between 1M items (where n=1_000_000), propose an algorithmic improvement to compute the top-100 most similar pairs efficiently. Describe time/space complexity and implementation strategies (approximate vs exact).

EasyTechnical

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Explain what quantization and pruning are for neural networks. For a model deployed to edge devices with strict memory limits, compare the trade-offs between 8-bit quantization and structured pruning in terms of accuracy, latency, memory footprint, and engineering complexity.

MediumTechnical

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You must choose cloud instance types and storage for hosting a CPU-bound model under a monthly budget constraint. Given expected 500k predictions/day with average processing time 50ms on a single vCPU and model size 2GB, propose a cost-optimized architecture and explain trade-offs between instance size, horizontal scaling, and storage options (SSD vs network blob).

HardTechnical

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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.

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