Explanation:

In Azure Machine Learning Designer, the Dataset output visualization feature is specifically used to explore and understand the distribution of values in potential feature columns before model training begins. This capability is critical for data exploration and preprocessing, two essential stages of the machine learning pipeline described in the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning learning paths.
When a dataset is imported into Azure Machine Learning Designer, users can right-click on the dataset output port and select "Visualize". This launches the dataset visualization pane, which provides detailed statistical summaries for each column, including:
* Data type (numeric, categorical, string, Boolean)
* Minimum, maximum, mean, and standard deviation values for numeric columns
* Frequency counts and distinct values for categorical columns
* Missing value counts
This visual inspection helps determine which columns should be used as features, which might need normalization or encoding, and which contain missing or irrelevant data. It is a vital step in ensuring the dataset is clean and ready for model training.
Let's examine why other options are incorrect:
* Normalize Data module is used to scale numeric data, not to visualize distributions.
* Select Columns in Dataset module is used to include or exclude columns, not to analyze them.
* Evaluation results visualization feature is used after model training to interpret performance metrics like accuracy or recall, not data distributions.
Therefore, based on official Microsoft documentation and AI-900 study materials, to explore the distribution of values in potential feature columns, you use the Dataset output visualization feature in Azure Machine Learning Designer.