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
0 practiced
Compare deploying inference on GPUs, TPUs, FPGAs, and CPU clusters for a multimodal model combining vision and language. Discuss development effort, latency, throughput, batchability, quantization support, cost, and maintenance effort. Recommend an approach for a near-real-time interactive application and justify trade-offs.
HardSystem Design
0 practiced
Design an inference pipeline resilient to bursty traffic that prevents OOMs and prioritizes high-value requests. Include queueing policies, rate limiting, adaptive shedding, circuit breakers, resource isolation (cgroups or equivalent), and graceful degradation strategies (fallback model, summarized responses). Discuss trade-offs between user experience, resource utilization, and implementation complexity.
EasyTechnical
0 practiced
You're deciding between a highly optimized, complex C++ implementation of a feature ingestion service and a simpler, slower Python version. Describe a decision framework that includes metrics to measure, experiments to run (benchmarks and POCs), cost of implementation and maintenance, and criteria (SLA, expected growth, team skill) that would make you accept or reject the C++ rewrite.
MediumTechnical
0 practiced
Compare data-parallel and model-parallel training for a 30B parameter transformer. Discuss communication patterns, required batch size for efficiency, gradient synchronization costs, memory distribution strategies, and when hybrid approaches (pipeline + tensor/model sharding) become necessary.
MediumTechnical
0 practiced
Your online learning system must ingest 10k updates/sec and keep models fresh with sub-minute staleness. Discuss batching vs per-update updates, eventual consistency, the effect of model staleness on downstream predictions, and resource trade-offs. How do you design the pipeline to gracefully degrade if input update traffic bursts above capacity?
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