According to the Microsoft Learn module “Explore fundamental principles of machine learning” and the AI-900 Official Study Guide, when building and evaluating a model (such as a clustering model) in Azure Machine Learning designer, data must be divided into two subsets:

Training dataset: Used to train the model so it can learn patterns and relationships in the data.

Testing dataset: Used to evaluate the model’s performance on unseen data, ensuring that it generalizes well and does not overfit.

In Azure ML Designer, this is typically done using the Split Data module, which separates the dataset into training and testing portions (for example, 70% training and 30% testing). After training, you connect the testing dataset to an Evaluate Model module to assess metrics such as accuracy, precision, or silhouette score (for clustering).

Other options are incorrect:

A. Split into features and labels: Clustering is an unsupervised learning technique, so it doesn’t use labeled data.

B. Use training dataset for evaluation: This would cause overfitting, as the model is being tested on the same data it learned from.

D. Use the original dataset for training and evaluation: Also causes overfitting, offering no measure of generalization.