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

0 practiced

Discuss dataset versioning as part of a robust ML QA process. What metadata should you store (hashes, sampling strategy, preprocessing steps, schema), how would you store/bundle large datasets for reproducible CI tests, and what automated checks would you run to detect dataset drift or corruption?

MediumTechnical

0 practiced

As a senior AI engineer, what specific checklist items would you require reviewers to verify in PRs that modify training code (data handling, augmentations, loss computation, optimizer updates)? Include tests to require, code style, logging, and documentation items to prevent common regressions.

MediumTechnical

0 practiced

Implement a pytest fixture named 'deterministic_env' that sets seeds for Python random, NumPy, and PyTorch (CPU and CUDA), sets torch.backends.cudnn.deterministic and benchmark flags appropriately, and yields control to tests before restoring previous state. Provide the fixture code and an example test that uses it.

HardSystem Design

0 practiced

Design a CI/CD pipeline architecture for a large AI model repository that includes pre-commit style checks, unit tests, GPU-enabled integration tests, model validation tests (quality and safety), dataset checks, packaging and artifact storage, and gated promotion to production. Describe stage dependencies, caching strategies for large datasets/artifacts, and trade-offs for running GPU tests on PRs.

MediumTechnical

0 practiced

Explain metamorphic testing and propose three metamorphic relations suitable for testing an image classification model's preprocessing and inference pipeline (e.g., invariance to small brightness changes, rotation symmetry for certain classes). For each relation, describe automated tests and how failures indicate problems.

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