Azure Data Scientists Associate Practice Exam

Which approach is best for making a model retraining code production-ready?

1. Copy and paste the code into a single script

2. Convert the code into one function

3. Convert the code into multiple functions

4. Leave the code in the notebook

The correct answer is: Convert the code into multiple functions

The best approach for making a model retraining code production-ready involves converting the code into multiple functions. This method promotes modular design, which enhances maintainability and readability. By breaking the code into smaller, reusable functions, it becomes easier to understand each component's purpose, facilitate testing, and isolate issues when they arise. In a production environment, different aspects of the model retraining process—such as data preprocessing, model training, validation, and performance evaluation—can be encapsulated into distinct functions. This modular design allows teams to work on individual functions, enable code reuse across different projects, and support better collaboration among data scientists and engineers. Additionally, structuring the code in this way aligns well with best practices in software engineering, such as separation of concerns and single responsibility principle. As a result, when a need arises to update or improve a specific part of the retraining process, it can be done independently without affecting the entire system. In contrast, having all the code in a single script or leaving it in a notebook can lead to complications in managing and updating the codebase. Single scripts can become unwieldy as they grow and may include tightly interwoven code that makes debugging and understanding difficult. Leaving code in a notebook can hinder reproducibility,