Code Quality & Technical Communication Questions
Best practices and principles for writing clean, maintainable code and communicating technical decisions clearly. Topics include code quality metrics, code reviews, refactoring, static analysis, testing strategies related to maintainability, documentation standards, API/documentation practices, and effective communication of design and architecture decisions.
EasyTechnical
0 practiced
You are onboarding to a repo with unclear dependency management. What files or practices would you add to pin dependencies and ensure consistent environments for training and serving models? Provide commands for creating a reproducible environment using conda and a lightweight option using pip-tools.
HardTechnical
0 practiced
Hard: A production model shows increased false positives after a new upstream data transformation was deployed. Walk through a triage and remediation plan that includes forensic logging, rollback options, modeling fixes, communication with stakeholders, and postmortem analysis steps.
MediumTechnical
0 practiced
Design logging and monitoring for a deployed fraud detection model. State which logs and metrics you would capture (technical and business), how you would store them, and what alerts/thresholds you would set for critical issues such as model degradation or data pipeline failure.
EasyTechnical
0 practiced
What static analysis and linting tools would you include in an ML project's CI to improve code quality? Explain the role of each tool and a configuration option or rule you would enable for each (e.g., flake8 max-line-length, bandit for security).
MediumTechnical
0 practiced
You must publish API documentation for a model inference endpoint using OpenAPI (Swagger). Describe which fields, examples, and error responses are critical for downstream engineers. Provide a short OpenAPI-like snippet for a POST /v1/predict endpoint that accepts JSON features and returns prediction and model_version.
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