Azure Data Scientists Associate Practice Exam

What component should be added to an Azure Machine Learning pipeline to split data for training and testing?

1. Join data component

2. Split data component

3. Normalize data component

4. Evaluate model component

The correct answer is: Split data component

The correct choice is to add a component specifically designed to split data, which is vital for preparing datasets for machine learning purposes. The "Split data component" is essential because it enables the division of the dataset into separate subsets for training and testing. This process is crucial for developing models that can generalize well to new, unseen data. Splitting the data allows the training procedure to utilize one portion of the data for learning the appropriate patterns and relationships, while the testing subset serves as a means to evaluate the model's performance. This method ensures that the model's effectiveness can be assessed objectively, as it is tested on data that it has not previously encountered. This component is particularly important in avoiding overfitting, where a model may perform exceptionally well on training data but poorly on new data because it has essentially memorized the training set instead of learning to generalize from it. By implementing a split, the data scientist can validate and refine their model effectively, resulting in a more robust and reliable outcome. Other components do not serve this specific function. For instance, joining data typically combines multiple datasets rather than splitting them, normalizing data adjusts the scale of features instead of partitioning datasets, and evaluating models assesses the performance of an already trained model rather