Data Science Life Cycle DY0-001 Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   A data scientist wants to record the exact state of code and data used to produce a specific model result, so that the experiment can be reproduced later. Which version control practice directly supports this goal?

   • AUsing a .gitignore file to exclude large data files
   • BCommitting frequently with descriptive messages
   • CCreating a new branch for each experiment
   • DTagging a commit with a version number
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2. 22foundation · easy

   A data science team is reviewing a colleague's code. The code contains a 200-line function that performs data loading, cleaning, feature engineering, and model training. Which clean code principle is being violated?

   • AKISS (Keep It Simple, Stupid)
   • BYAGNI (You Aren't Gonna Need It)
   • CDRY (Don't Repeat Yourself)
   • DSingle Responsibility Principle
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4. 23application · medium

   A data science team maintains a shared codebase for data preprocessing. They want to improve code readability and maintainability. Which set of practices best achieves this goal?

   • AUse short variable names like 'x' and 'y' to reduce typing, and keep all logic in a single file.
   • BWrite long scripts with comments explaining each line, and avoid breaking code into functions.
   • CUse meaningful function names, add docstrings, and refactor repeated logic into reusable functions.
   • DMinimize comments and docstrings to reduce clutter, and duplicate code where necessary for clarity.
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5. 24application · medium

   A data science team is reviewing a colleague's code. The code has many global variables, no functions, and no comments. Which clean code practice should the team recommend?

   • ARefactor the code into small, single-purpose functions and use local variables.
   • BKeep the code as is, since it works correctly.
   • CRename global variables to be more descriptive.
   • DAdd comments to explain each global variable.
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6. 25application · medium

   A data science team is developing a model that will be maintained by multiple team members over time. They want to ensure that the code is easy to understand and modify. Which approach best supports this maintainability goal?

   • AFollow a consistent coding style, use modular functions, and include clear documentation.
   • BUse a single large script that contains all logic, and avoid refactoring.
   • COptimize code for maximum performance, even if it reduces readability.
   • DWrite code quickly without comments, and use global variables to reduce parameter passing.
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