Debugging and Code Optimization Questions
Practical debugging skills and techniques for improving code performance and complexity. Topics include tracing and reproducing bugs, stepping through execution, reasoning about time and space complexity, refactoring for performance, and applying algorithmic optimizations. Candidates should be able to demonstrate logical debugging approaches and make safe, measurable performance improvements to working code.
EasyTechnical
87 practiced
Explain a simple race condition that could occur when using Python's threading to increment a shared counter across requests. Provide an example in words or pseudocode, describe why it occurs (GIL doesn't prevent it here), and show how to fix it with locking or atomic primitives.
MediumSystem Design
75 practiced
A feature store computes expensive features repeatedly. Propose a caching strategy to reduce recomputation: design the cache key scheme, invalidation policy, storage layer (in-memory vs disk), and how to measure cache hit/miss impact on latency and correctness.
HardTechnical
97 practiced
Derive the time and space complexity for a custom batching algorithm that groups variable-length sequences into buckets to minimize padding. Prove correctness of your greedy bucketing heuristic and state worst-case bounds compared to naive padding-all approach.
HardTechnical
105 practiced
During distributed synchronous training you notice that one worker's loss explodes. Propose a debugging approach to find if data skew, hardware differences, or faulty preprocessing explains the divergence. Include metrics to collect, how to compare per-worker histograms, and mitigations to re-sync workers.
MediumTechnical
78 practiced
You're given a Python codebase and asked to profile a slow function. Explain how to interpret flame graphs produced by a sampling profiler and how they differ from call-graph outputs from deterministic profilers. How would you use both to prioritize optimization work?
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