This sequence represents the fundamental workflow for supervised machine learning, which is visually implemented in the Azure Machine Learning designer.

1. First, data must be ingested and prepared (e.g., cleaned, transformed).
2. Next, this dataset is split into a training set (to teach the model) and a validation/test set (to test it).
3. The model is then trained using only the training data.
4. Finally, the trained model's performance is assessed by evaluating it against the unseen validation dataset. Evaluating against the "original dataset" would be incorrect as it includes the training data, leading to an over-optimistic and invalid performance metric (overfitting).

Microsoft Press Store (for Exam DP-100): In the guide "Explore data and train models," the process for creating a training pipeline in the designer is detailed. It specifies the sequence:

Use modules to "clean and transform your data" (Prepare).

"Employ the Split Data module to divide your dataset into training and validation sets."

"Connect your preprocessed and split datasets to the Train Model module."

"Examine the output of the Evaluate Model module to assess the performance."

(Source: Microsoft Press Store, "Explore data and train models," Article 2, Skill 2.2: "Create models by using the Azure Machine Learning Designer," sections "Create a training pipeline" and "Here is a more detailed set of instructions...")

Microsoft Learn (Azure ML Documentation): The tutorial "Tutorial: Train a no-code regression model in the designer" demonstrates this logical flow. A dataset is added, prepared (e.g., Select Columns in Dataset, Clean Missing Data), and then split using the Split Data component. The outputs of the split data component are then fed into the Train Model component, which is subsequently connected to a Score Model and Evaluate Model component.

(Source: Microsoft Learn, "Tutorial: Train a no-code regression model in Azure Machine Learning designer," sections "Prepare data," "Split the data," "Train the model," and "Test, score, and evaluate the model.")

Microsoft Learn (Component Reference): The official documentation for the Train Model component states it has two inputs: an untrained algorithm and the "training dataset." It explicitly notes, "After training is completed, use the trained model with one of the scoring components, to make predictions on new data," which is the function of the Evaluate Model component.

(Source: Microsoft Learn, "Train Model: Component Reference," sections "How the training process works" and "How to use Train Model.")