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.
EasyTechnical
0 practiced
Which production metrics would you instrument to detect performance regressions in a model-serving endpoint? Include latency percentiles (p50/p95/p99), error rates, GPU/CPU utilization, queue lengths, batch sizes, model version distribution, and input feature distributions. For each metric, explain what signal it provides and give an example threshold you might alert on.
EasyTechnical
0 practiced
Explain the trade-offs between concurrency models (single-threaded async/event-loop, multi-threading, multi-processing) for different AI backend components: CPU-bound preprocessing, I/O-bound feature fetch, and GPU-bound inference. Which model would you choose for a CPU-bound preprocessing service and why?
HardSystem Design
0 practiced
Design a multi-tenant LLM inference platform that must serve 500 QPS with P95 generation latency <150ms for single-token responses. Requirements: support models from 1B to 70B parameters, batching and dynamic batching, model versioning, GPU pooling and autoscaling within a single region, per-tenant isolation and cost accounting, cold-start minimization, and safe multi-tenancy. Describe components, scheduling and batching strategy, GPU allocation policies, caching, failure modes, monitoring, and trade-offs between isolation and utilization.
EasyTechnical
0 practiced
List common micro-optimizations you could apply to Python inference code in a model-serving context (e.g., minimize per-request allocations, reuse buffers, prefer NumPy/Numba primitives, use memoryview, avoid Python-level loops). For each, briefly describe typical speedup range, maintainability cost, and when it is worthwhile compared to a higher-level change.
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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