AI-900: Optimizing Azure ML Pipelines: Unraveling the Role of the Split Data Module

Delve into Azure Machine Learning pipelines and understand the pivotal function of the Split Data module. Explore its role in creating training and validation datasets, unlocking the potential for enhanced model training and scoring. Elevate your understanding of this crucial component for efficient and effective machine learning workflows.

Question

You have an Azure Machine Learning pipeline that contains a Split Data module. The Split Data module outputs to a Train Model module and a Score Model module. What is the function of the Split Data module?

A. selecting columns that must be included in the model
B. creating training and validation datasets
C. diverting records that have missing data
D. scaling numeric variables so that they are within a consistent numeric range

Answer

B. creating training and validation datasets

Explanation

The correct answer is B. creating training and validation datasets.

The Split Data module in Azure Machine Learning pipeline is used to divide a dataset into two distinct sets. This component is useful when you need to separate data into training and testing sets. You can also customize the way that data is divided. Some options support randomization of data. Others are tailored for a certain data type or model type.

The Train Model module uses the first output of the Split Data module as the training dataset. The Score Model module uses the second output of the Split Data module as the validation dataset. The validation dataset is used to evaluate the performance of the trained model on unseen data.

The other options are not the functions of the Split Data module. Option A is the function of the Select Columns in Dataset module, which allows you to select a subset of columns from a dataset. Option C is the function of the Clean Missing Data module, which allows you to handle missing data by replacing, removing, or flagging them. Option D is the function of the Normalize Data module, which allows you to scale numeric variables to a common range.

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