A data scientist is collaborating with a team on a shared repository for a machine learning project. They need to ensure that changes to the feature engineering code are reviewed before merging, and that the code follows consistent naming conventions and includes tests for new functions. Which Git workflow and practices should they adopt?

Use a single shared branch, use descriptive variable names, and skip unit tests to save time.

Use a feature-branch workflow with pull requests, enforce a style guide via linters, and require unit tests to pass before merge.

Commit directly to the main branch, rely on peer review after the fact, and write tests only for the final model.

Fork the repository, merge changes without review, and rely on documentation for code quality.

A data science team is developing a model that will be handed off to a different team for maintenance. The handoff team will need to understand the code quickly. The original team is under time pressure to finish. What is the best way to ensure a smooth handoff?

Provide a detailed presentation to the handoff team, but keep the code as is.

Refactor the code into a single large script to make it easier to share.

Rely on the handoff team to ask questions if they need clarification.

Write clear documentation and docstrings, and follow a consistent coding style throughout the project.

A data science team is using Git for version control. They notice that large model files are bloating the repository and slowing down clones. They still need to track changes to these files. What is the best solution?

Use Git submodules to include large files from a separate repository.

Remove large files from the repository and store them on a shared drive.

Use Git LFS to store large files outside the main repository while keeping version history.

Compress the large files before committing them to the repository.

A data science team is transitioning from using shared spreadsheets to a version control system for their code and notebooks. They want to ensure that every change is attributable to a specific author and that tests are run before changes are accepted. Which practices should they implement?

Use a version control system that only tracks file changes, and manually run tests after merging.

Use Git with commit messages that include the author's name, and set up a CI pipeline that runs unit tests on every pull request.

Use Git but allow direct commits to the main branch, and rely on code reviews after the fact.

Store code in a shared folder with file names that include the author's initials, and run tests only on the final release.

CompTIADataX

Domain 4Objective 5

Data Science Life Cycle DY0-001 Practice Questions (Page 2)

Part of the Operations and processes domain, which accounts for 22% of the DY0-001 exam. CompTIA does not publish an official question count, but from its 165-minute exam (~65–110 total, ~14–24 in this domain), expect 2–3 from this objective — we provide 25 practice questions to prepare you well beyond it. (estimate)

25questions here

5free pages

4concepts

22%of the exam

Questions 6–10

6application · medium
A data scientist is collaborating with a team on a shared repository for a machine learning project. They need to ensure that changes to the feature engineering code are reviewed before merging, and that the code follows consistent naming conventions and includes tests for new functions. Which Git workflow and practices should they adopt?
Select an answer first
7expert · hard
A data science team is developing a model that will be handed off to a different team for maintenance. The handoff team will need to understand the code quickly. The original team is under time pressure to finish. What is the best way to ensure a smooth handoff?
Select an answer first
8expert · hard
A data science team is using Git for version control. They notice that large model files are bloating the repository and slowing down clones. They still need to track changes to these files. What is the best solution?
Select an answer first
9application · medium
A data science team is transitioning from using shared spreadsheets to a version control system for their code and notebooks. They want to ensure that every change is attributable to a specific author and that tests are run before changes are accepted. Which practices should they implement?
Select an answer first
10foundation · easy
A data science team is adopting a workflow model that emphasizes iterative cycles of building, testing, and refining models, with each cycle producing a potentially usable increment. Which workflow model does this describe?
Select an answer first

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Data Science Life Cycle DY0-001 Practice Questions (Page 2)

Questions 6–10

A data science team is developing a model that will be handed off to a different team for maintenance. The handoff team will need to understand the code quickly. The original team is under time pressure to finish. What is the best way to ensure a smooth handoff?

A data science team is using Git for version control. They notice that large model files are bloating the repository and slowing down clones. They still need to track changes to these files. What is the best solution?

A data science team is transitioning from using shared spreadsheets to a version control system for their code and notebooks. They want to ensure that every change is attributable to a specific author and that tests are run before changes are accepted. Which practices should they implement?

A data science team is adopting a workflow model that emphasizes iterative cycles of building, testing, and refining models, with each cycle producing a potentially usable increment. Which workflow model does this describe?

Keep studying — Operations and processes