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.

HardSystem Design

0 practiced

Architect an end-to-end testing, debugging, and optimization framework for training a large transformer model across multi-node, multi-region clusters using spot instances. Include reproducibility, sharded checkpoints, profiling hooks, automatic failover and requeueing, cost control, and verification tests that assert model quality after resume. Describe components and interactions.

MediumTechnical

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Explain how to design fair and reproducible benchmarks comparing different model implementations for throughput and latency. Discuss warm-up runs, fixed hardware, multiple iterations, percentiles (p50/p95/p99), controlling for batching and JIT differences, and how to report variance and statistical confidence.

EasyTechnical

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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.

EasyTechnical

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In Python, implement set_seeds(seed: int) that sets seeds for reproducible experiments across: Python's random, NumPy, and PyTorch (CPU and CUDA). Include handling for torch.backends.cudnn (deterministic vs benchmark) and mention common caveats for multi-process DataLoader workers and distributed training. Provide a short code example showing usage.

HardTechnical

0 practiced

You are on-call for an ML service that starts producing systematic mispredictions that cost revenue. Describe your incident response: immediate mitigation steps, triage checklist, short-term rollback/containment, data and model forensics to find root cause, postmortem procedures, and concrete test or CI changes you'd implement to prevent recurrence.

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