You have a dataset. You need to build an Azure Machine Learning classification model that will identify defective products. What should you do first?

Before any data preparation, feature engineering, model selection, or training can occur, the raw dataset must first be loaded and ingested into the machine learning environment, making it available for use.

Creating a clustering model is irrelevant as the problem specifies building a classification model, and it is a model creation step, not the first data access step.

Splitting the data into training and testing datasets is a crucial preprocessing step, but it can only be performed after the dataset has been successfully loaded.

Creating a classification model is a later stage in the machine learning workflow, which comes after data loading, preparation, and often feature engineering.