Data Science Life Cycle DY0-001 Practice Questions (Page 3)

Questions 11–15

1. 11expert · hard

   A data science team is collaborating on a project with multiple contributors. They are experiencing merge conflicts frequently because team members are editing the same files. They also want to ensure that code quality is maintained. Which approach best addresses these issues?

   • AHave all team members commit directly to the main branch to avoid merge conflicts.
   • BUse a single shared branch and coordinate via a chat channel to avoid conflicts.
   • CUse Git LFS for all files to reduce merge conflicts.
   • DUse feature branches with small, focused commits, and enforce code reviews through pull requests.
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2. 12application · medium

   A data science team is developing a library of data transformation functions. They want to ensure that changes to one function do not break other functions that depend on it. What is the best practice to achieve this?

   • ADocument the expected behavior of each function and rely on code reviews.
   • BWrite integration tests only for the final pipeline, and run them manually before releases.
   • CUse version control to revert changes if something breaks, without writing tests.
   • DWrite unit tests for each function and run them as part of a CI pipeline on every commit.
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4. 13foundation · easy

   A data science project has well-defined, stable requirements and a clear sequence of phases: data collection, data cleaning, feature engineering, model training, and deployment. Which workflow model is most appropriate for this scenario?

   • AAgile
   • BWaterfall
   • CSpiral
   • DCRISP-DM
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5. 14expert · hard

   A data science team is developing a model for a regulated industry. They must provide evidence that the model was developed following a documented process, and that each version of the model can be traced to specific code and data. They also need to ensure that every code change is tested before being incorporated. Which combination of practices best meets these requirements?

   • AUse a stage-gate workflow with documented approvals, use Git tags for each model version, and run unit tests in a CI pipeline on every commit.
   • BUse a CRISP-DM workflow, store code in a shared drive, and write unit tests only for the final model.
   • CUse an agile workflow with daily standups, use Git branches for features, and rely on manual testing before releases.
   • DUse a waterfall model, use Git for version control, and skip unit tests to meet deadlines.
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6. 15application · medium

   A data science team is working on a project where the business requirements are expected to change frequently. They need a workflow that allows them to revisit earlier phases without disrupting the overall project. Which workflow model is most suitable?

   • AA linear waterfall model that proceeds sequentially through phases.
   • BA one-time pipeline that runs from data collection to deployment without revisiting.
   • CA batch processing model that runs all steps in a fixed order.
   • DAn iterative workflow like CRISP-DM that allows cycling back to previous phases.
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