CompTIA DataX DY0-001 (V1) Practice Question

Your data-science team relies on GitHub pull requests for code review. The repository includes several Jupyter notebooks (.ipynb) that analysts execute locally while experimenting. Because each notebook serializes execution counts, images, and other outputs, even a one-line code change produces thousands of lines in the Git diff and frequent merge conflicts in the output cells. Leadership insists that notebooks must stay under version control so reviewers can still inspect code and markdown changes, but the execution outputs should never be committed. You need a solution that removes the noisy output automatically for every contributor without forcing them to delete notebooks or learn a new interface. Which Git-based approach best meets these requirements?

  • Require contributors to export every notebook as a plain Python script and commit only the generated script files.

  • Mark *.ipynb files as binary in .gitattributes so Git stores them but suppresses text diffs during reviews.

  • Add *.ipynb to the project's .gitignore so notebooks are no longer tracked in the repository.

  • Configure a Git filter or pre-commit hook that strips all output cells and execution metadata from .ipynb files before each commit is finalized.

CompTIA DataX DY0-001 (V1)
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