Discover the two key components you can drag onto a canvas in Azure Machine Learning designer to build your machine learning pipelines. Learn how datasets and modules are essential for creating powerful AI models in Azure.
Table of Contents
Question
Which components can you drag onto a canvas in Azure Machine Learning designer? Select two correct options.
A. dataset
B. pipeline
C. compute
D. module
Answer
A. dataset
D. module
Explanation
In Azure Machine Learning designer, you can drag two main components onto the canvas to build your machine learning pipelines:
A. Dataset
A dataset represents the data you will use to train and test your machine learning model. In the designer, you can add a dataset component to your canvas, which allows you to import data from various sources such as Azure Blob Storage, Azure SQL Database, or local files. The dataset component serves as the input for your machine learning pipeline.
D. Module
Modules are the building blocks of your machine learning pipeline in the designer. Each module represents a specific operation or algorithm that you can apply to your data. For example, you can use a data preprocessing module to clean and transform your data, a feature selection module to choose the most relevant features, or a machine learning algorithm module to train your model. By dragging and connecting different modules on the canvas, you can create a complete end-to-end machine learning pipeline.
The other two options, B (pipeline) and C (compute), are not components that you can directly drag onto the canvas in Azure Machine Learning designer. A pipeline is the entire workflow you create by connecting datasets and modules, while compute refers to the underlying computational resources used to run your pipeline.
In summary, the two key components you can drag onto a canvas in Azure Machine Learning designer are datasets and modules, which together allow you to build powerful machine learning pipelines for various AI applications.
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