AI-900: What Is Azure Machine Learning Designer Used For?

Discover how Azure Machine Learning designer enables you to create ML models using a visual drag-and-drop interface. Learn key features for the AI-900 certification exam.

Question

Azure Machine Learning designer can be used to create machine learning models by. Select the answer that correctly completes the sentence.

A. Automatically selecting an algorithm to build the most accurate model.
B. Automatically performing common data preparation tasks.
C. Using a code-first notebook experience.
D. Adding and connecting modules on a visual canvas.

Answer

Azure Machine Learning designer is a web-based tool that allows you to create machine learning models using a drag-and-drop visual interface, without requiring extensive coding knowledge. The correct answer for how the designer is used to build models is:

D. Adding and connecting modules on a visual canvas.

Explanation

Key features and benefits of Azure Machine Learning designer:

1. Visual interface: The designer provides an intuitive canvas where you can add and connect various pre-built or custom modules to create your machine learning pipeline. This visual approach makes it easier to understand the model building process.
2. Drag-and-drop modules: The designer includes a wide range of modules for data preparation, feature selection, model training, evaluation, and deployment. You simply drag the desired modules onto the canvas and connect them to define the workflow.
3. No-code/low-code: With the designer, you can build models without writing extensive code. The visual interface abstracts away much of the underlying code, making it accessible to a broader audience, including business analysts and domain experts.
4. Built-in algorithms: The designer offers a variety of pre-built machine learning algorithms, such as regression, classification, and clustering, which you can easily incorporate into your pipeline by adding the corresponding module.
5. Experiment tracking: The designer automatically tracks your experiments, including the pipeline, hyperparameters, and performance metrics. This allows you to easily compare different versions of your model and choose the best one.

While the designer does offer some automation capabilities, such as suggesting modules based on your data or automatically handling missing values, the core functionality revolves around the visual composition of the machine learning pipeline. Therefore, adding and connecting modules on a canvas is the most accurate description of how Azure Machine Learning designer is used to create models.

Azure Machine Learning designer can be used to create machine learning models by:

Adding and connecting modules on a visual canvas.

Azure Machine Learning designer provides a drag-and-drop interface to build machine learning pipelines by connecting different modules visually. It does not automatically select algorithms or perform data preparation tasks, nor is it primarily a code-first environment.

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