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
Write a Python decorator profile_and_log that measures execution time and memory usage of a function, logs the results using the logging module, and preserves the wrapped function's metadata. Show a short example of applying it to a model training function and explain how this helps maintainability and incident debugging.
HardTechnical
0 practiced
How would you set realistic test coverage targets for a heterogeneous data science repository that contains notebooks, ETL scripts, feature engineering pipelines, model training code, and production scoring modules? Propose a measurement approach (what to include/exclude), a coverage policy for PRs, and alternatives to line coverage that better reflect quality (e.g., mutation testing, critical-path tests).
MediumBehavioral
0 practiced
Describe a time (or a hypothetical scenario) where you had to explain technical trade-offs of a model deployment (latency vs accuracy vs cost) to non-technical stakeholders. How did you structure the conversation, what visualizations or metrics did you present, and how did you reach a decision aligned with business goals?
EasyTechnical
0 practiced
Notebooks are commonly used by data scientists. Describe the common 'code smells' and maintainability issues found in Jupyter notebooks and explain why each one reduces code quality for productionizing analytics or models. For each smell, give a short corrective action or refactor approach (example: hidden state, long monolithic cells, inline large outputs, untracked data downloads).
MediumSystem Design
0 practiced
Design an experiment tracking system for your data science team that stores experiments with parameters, code commit hashes, environment (package versions), input dataset identifiers, artifacts (models, metrics, plots), and allows search and reproducible re-runs. Describe core storage components, APIs, UI features, and how you'd integrate it with CI/CD and model registry.
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