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.

MediumTechnical

49 practiced

Explain mixed-precision training (FP16/FP32) and gradient scaling. What performance gains and numerical stability risks exist? Describe when you would enable mixed precision in production retraining jobs and how you would validate correctness.

HardTechnical

54 practiced

Design an experiment to decide whether to invest engineering effort in an algorithmic improvement that reduces CPU time by 30% vs a systems fix that reduces network serialization time by 60% but is riskier to deploy. Describe metrics to compare business impact and an A/B test plan.

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.

MediumTechnical

61 practiced

You need to reduce a large model's serving latency and memory footprint. Compare and prioritize the following techniques for this production scenario: pruning, quantization-aware training, knowledge distillation, and architecture change (smaller model). Justify choices by accuracy risk, implementation effort, and expected gains.

EasyTechnical

47 practiced

A model serving endpoint experiences high throughput but occasional latency spikes. Define the difference between latency and throughput, and describe why optimizing for one can impact the other. Give two practical backend changes that improve throughput but may increase latency, and two that reduce latency but may hurt throughput.

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