A data science team is working on a project with a tight deadline. They need to quickly prototype multiple modeling approaches while still tracking changes and collaborating effectively. Which workflow model and version control practice best supports this need?

Use a spiral model, and use a shared network drive for code storage.

Use an iterative workflow like CRISP-DM, and use Git branches for each experimental approach.

Use an agile scrum workflow, and avoid version control to speed up development.

Use a linear waterfall model, and commit all changes to the main branch.

A data science team is working on a project with multiple experiments. They want to keep track of which code version produced each experimental result. What is the best practice?

Use Git tags or commits to mark the exact code version used for each experiment, and record the tag in the experiment log.

Save the code in a zip file with a timestamp in the filename.

Keep a spreadsheet listing the code changes made for each experiment.

Rely on the model's performance metrics to infer which code version was used.

A data scientist writes a unit test for a function that calculates the mean of a list. The test asserts that mean([2, 4, 6]) == 4.0. What is the primary purpose of this unit test?

To ensure the function is used in production

To validate that the function produces the expected output for a known input

To measure the performance of the function

To document the function's implementation details

A data science team uses a testing framework to run unit tests automatically whenever a developer commits code. What is a key benefit of this practice?

It ensures the code is free of all bugs

It speeds up the training of machine learning models

It catches regressions early in the development cycle

It eliminates the need for code reviews

A data science team is building a predictive model for customer churn. They follow a workflow model where each phase (data collection, cleaning, feature engineering, modeling, evaluation) must be completed and approved before the next phase begins. The team wants to ensure that any changes to the code or data processing steps are tracked and that each model version can be reproduced. Which combination of practices best supports this workflow?

Implement a waterfall model, use a shared network drive for code backups, and write integration tests instead of unit tests.

Adopt an agile iterative workflow, commit code to a shared branch without tags, and rely on manual testing of the final model.

Use a linear workflow with phase gates, maintain a Git repository with tagged releases for each approved phase, and write unit tests for data cleaning functions.

Use a CRISP-DM workflow, store all code in a single script without version control, and perform unit testing only after model deployment.

CompTIADataX

Domain 4Objective 5

Data Science Life Cycle DY0-001 Practice Questions (Page 4)

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 16–20

16application · medium
A data science team is working on a project with a tight deadline. They need to quickly prototype multiple modeling approaches while still tracking changes and collaborating effectively. Which workflow model and version control practice best supports this need?
Select an answer first
17application · medium
A data science team is working on a project with multiple experiments. They want to keep track of which code version produced each experimental result. What is the best practice?
Select an answer first
18foundation · easy
A data scientist writes a unit test for a function that calculates the mean of a list. The test asserts that mean([2, 4, 6]) == 4.0. What is the primary purpose of this unit test?
Select an answer first
19foundation · easy
A data science team uses a testing framework to run unit tests automatically whenever a developer commits code. What is a key benefit of this practice?
Select an answer first
20application · medium
A data science team is building a predictive model for customer churn. They follow a workflow model where each phase (data collection, cleaning, feature engineering, modeling, evaluation) must be completed and approved before the next phase begins. The team wants to ensure that any changes to the code or data processing steps are tracked and that each model version can be reproduced. Which combination of practices best supports this workflow?
Select an answer first

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