Debugging Testing and Optimization Questions

Core engineering skills for identifying, diagnosing, testing, and improving code correctness and performance. Covers approaches to finding and fixing bugs including reproducible test case construction, logging, interactive debugging, step through debugging, and root cause analysis. Includes testing strategies such as unit testing, integration testing, regression testing, test driven development, and designing tests for edge cases, boundary conditions, and negative scenarios. Describes performance optimization techniques including algorithmic improvements, data structure selection, reducing time and space complexity, memoization, avoiding unnecessary work, and parallelism considerations. Also covers measurement and verification methods such as benchmarking, profiling, complexity analysis, and trade off evaluation to ensure optimizations preserve correctness and maintainability.

HardTechnical

0 practiced

Discuss trade-offs between model-level optimizations (pruning, quantization, distillation) and system-level optimizations (batching, caching, sharding). Cover impact on accuracy, latency, maintainability, testability, rollback complexity, and suggested testing strategies to validate each optimization end-to-end.

HardTechnical

0 practiced

A Python data augmentation function loops over images applying affine transforms and becomes the training bottleneck. Given a naive loop-based implementation, implement an optimized version using Numba (njit with parallel=True) to improve CPU throughput. Show before/after sketches and explain where parallelism helps.

MediumTechnical

0 practiced

Describe how to profile a training job to identify CPU vs GPU bottlenecks. Include which tools you would use (torch.profiler, Nsight Systems, nvidia-smi), how to profile dataloader (num_workers, pin_memory), how to measure data transfer times, and how to interpret results to target optimizations.

MediumTechnical

0 practiced

Demonstrate how to apply gradient checkpointing in PyTorch to reduce memory usage: provide a small example where parts of a sequential model are wrapped with torch.utils.checkpoint.checkpoint and explain the trade-offs (extra compute for lower memory). Include a short code snippet showing usage.

EasyTechnical

0 practiced

Design a test plan for model inputs at boundaries and edge cases. For an NLP classification model, enumerate concrete tests for: empty inputs, extremely long sequences, max-length trimming, rare/unseen tokens, nulls, adversarial punctuation-only strings, and malformed encodings. Describe how you'd implement these tests and integrate them into CI.

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