Data Science Life Cycle DY0-001 Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data scientist writes a function that calculates the mean of a list of numbers. They want to ensure the function behaves correctly for edge cases like empty lists and non-numeric inputs. What is the most appropriate approach?

   • ARely on the function's documentation to describe expected behavior.
   • BManually test the function with a few sample inputs in a Jupyter notebook.
   • CWrite unit tests that cover normal cases, empty lists, and invalid inputs, and run them automatically.
   • DAdd print statements inside the function to debug during execution.
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2. 2expert · hard

   A data science team is working on a project with a fixed budget and a strict deadline. They need to balance the need for thorough testing and clean code with the need to deliver quickly. Which approach best balances these competing constraints?

   • AUse a CRISP-DM workflow and write tests for every function, including trivial ones.
   • BUse a lightweight iterative workflow, write unit tests for critical functions, and refactor code only when necessary.
   • CUse a comprehensive waterfall model with extensive documentation and testing at every phase.
   • DSkip unit testing to save time, and rely on manual testing at the end.
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4. 3foundation · easy

   A data scientist is writing a function to calculate the mean of a list of numbers. Which function name best follows clean code practices?

   • Adef m(d):
   • Bdef do_it(x):
   • Cdef function1(data):
   • Ddef calc_mean(data):
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5. 4foundation · easy

   A data science team uses Git to manage their project. They want to ensure that changes to code are reviewed and approved before being merged into the main branch. Which Git workflow feature enables this?

   • APull requests
   • BGit rebase
   • CGit stash
   • DGit clone
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6. 5expert · hard

   A data science team is refactoring a legacy codebase to improve maintainability. The code currently has long functions, duplicated logic, and no tests. They want to make changes safely without breaking existing functionality. What is the best approach?

   • ARewrite the entire codebase from scratch using clean code principles.
   • BAdd unit tests only for new features, and refactor only the parts that are not working.
   • CWrite characterization tests for existing functions, then refactor incrementally with version control checkpoints.
   • DRefactor the code without tests, relying on code reviews to catch issues.
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