Microsoft DP-100 Designing and Implementing a Data Science Solution on Azure 02 31Oct2024 - Practice Quiz

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Question 1 of 30

In the context of Azure Machine Learning Pipelines, a data scientist is tasked with building a pipeline that automates the process of data preparation, model training, and evaluation. The pipeline must include steps for data ingestion, feature engineering, model training, and model evaluation. Given that the data scientist wants to ensure that the pipeline can be reused and easily modified for different datasets and models, which of the following practices should be prioritized when designing the pipeline?

Implement modular components for each step of the pipeline, allowing for easy swapping of datasets and models.

Create a monolithic pipeline that includes all steps in a single script to minimize complexity.

Use hard-coded values for parameters to ensure consistency across runs.

Limit the use of version control to only the final model to reduce overhead.

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