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.

HardSystem Design

0 practiced

Hard: Design a robust model-versioning and provenance system. Your design should capture model binary, training data snapshot, preprocessing code, hyperparameters, training commit hash, and evaluation metrics. Explain how you'd support reproducible re-training and quick rollbacks.

EasyTechnical

0 practiced

Describe 5 code quality metrics that are relevant for an ML codebase (for example, cyclomatic complexity, test coverage). For each metric, explain what aspect of maintainability it captures and provide one example of when the metric can be misleading in ML projects.

EasyTechnical

0 practiced

You inherit a Python training script with inconsistent style and no documentation. List the PEP8 conventions you would enforce across the ML repo and refactor the function below to conform to those conventions and add a clear docstring describing purpose, args, and return value:

python

def train(x,y,lr=0.01,epochs=10):
  for i in range(epochs):
    loss = model.forward(x,y)
    model.update(lr)
  return model

Explain the changes you made and why they improve maintainability.

EasyTechnical

0 practiced

Write three concise rules you would include in a documentation standard for ML models in your team. The standard should cover code-level docs (docstrings), API docs, and model-level documentation (model card). Provide an example outline for a model card for a binary classifier used in production.

HardTechnical

0 practiced

Hard: Design a policy and technical workflow for handling third-party license compliance in ML projects that use open-source libraries and pretrained models. Cover scanning, approval workflow, allowed/prohibited licenses, and CI enforcement.

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