Azure Data Scientists Associate Practice Exam

In Azure Machine Learning Designer, which component is used to merge two datasets using a database style join operation?

1. Split data component

2. Join data component

3. Normalize data component

4. Two-class decision forest component

The correct answer is: Join data component

The use of the Join Data component in Azure Machine Learning Designer is essential for combining two datasets based on a key attribute, facilitating a database-style join operation. This component allows users to specify the type of join they wish to perform, such as inner, outer, left, or right join. This flexibility enables data scientists to merge datasets while preserving the required information and discarding the unnecessary data, based on the specific analysis needs. Other components mentioned serve different purposes. For instance, the Split Data component is specifically designed for partitioning a dataset into training and testing subsets, which is a fundamental step in the model evaluation process but unrelated to merging datasets. The Normalize Data component is utilized for scaling or transforming features within a dataset to a certain range or distribution, ensuring that no single feature dominates the model due to its scale, while the Two-class decision forest component is a machine learning algorithm for binary classification tasks, which is not related to data merging. Thus, the Join Data component is uniquely suited to facilitate the merging of datasets through various join operations, making it the correct choice for the scenario presented.