Code Quality and Debugging Practices Questions

Focuses on writing maintainable, readable, and robust code together with practical debugging approaches. Candidates should demonstrate principles of clean code such as meaningful naming, clear function and module boundaries, avoidance of magic numbers, single responsibility and separation of concerns, and sensible organization and commenting. Include practices for catching and preventing bugs: mental and unit testing of edge cases, assertions and input validation, structured error handling, logging for observability, and use of static analysis and linters. Describe debugging workflows for finding and fixing defects in your own code including reproducing failures, minimizing test cases, bisecting changes, using tests and instrumentation, and collaborating with peers through code reviews and pair debugging. Emphasize refactoring, test driven development, and continuous improvements that reduce defect surface and make future debugging easier.

MediumTechnical

48 practiced

You observe intermittent flakiness in unit and integration tests that pass locally but fail occasionally in CI for GPU-based training steps. Describe your step-by-step approach to detect, reduce, and isolate flaky tests in an ML codebase. Include tooling, CI strategies, and ways to rewrite tests to be deterministic or resilient.

HardTechnical

42 practiced

Create a checklist and automated gating policy for reviewing PRs that change critical training hyperparameters (batch size, learning rate, weight decay, gradient-accumulation steps). Which automated tests, small-scale experiments, and documentation updates should be required before such a PR is merged?

MediumTechnical

41 practiced

Implement a Python decorator or context manager using the standard logging module that measures and logs execution time and contextual metadata (dataset id, epoch, step) for wrapped training or evaluation functions. Show usage examples in a training loop and explain how to keep logs concise and structured for downstream parsing.

EasyTechnical

33 practiced

Describe a Test-Driven Development (TDD) approach to implement a metric function 'top_k_accuracy(preds, labels, k)' in Python for classification tasks. Specify the tests you would write first (including edge cases) using pytest and then summarize the minimal implementation that satisfies them.

MediumTechnical

68 practiced

Write a set of pytest parametrized tests for a tokenization function 'tokenize(text)' that cover edge cases: empty string, multiple consecutive whitespaces, non-ASCII characters (emoji), very long input, and mixed punctuation. Use pytest.mark.parametrize and describe expected tokens for each case.

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