Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and the Microsoft Learn module "Describe Azure Machine Learning and Automated ML," Azure Machine Learning designer (formerly known as Azure Machine Learning Studio) is a drag-and-drop, low-code/no-code environment that allows users to create, train, and evaluate machine learning models visually - without the need for extensive programming knowledge.
The designer provides a visual interface, known as the canvas, where users can:
* Import and prepare data using modules for data transformation and cleaning.
* Split data into training and testing datasets.
* Select and configure algorithms (classification, regression, or clustering).
* Train and evaluate the model.
* Deploy the model as a web service directly from the designer.
The official Microsoft Learn content emphasizes that "Azure Machine Learning designer enables users to build, test, and deploy models by adding and connecting prebuilt modules on a visual interface." This allows business analysts, data professionals, and beginners to experiment with machine learning workflows without writing code.
By comparison:
* Automatically performing common data preparation tasks refers to Automated ML, not the designer.
* Automatically selecting an algorithm is also part of Automated ML, which optimizes models algorithmically.
* Using a code-first notebook experience applies to Azure Machine Learning notebooks, intended for data scientists familiar with Python and SDKs.
Therefore, as per the AI-900 study guide and Microsoft Learn documentation, the verified and correct answer is:
# Adding and connecting modules on a visual canvas, which accurately describes how Azure Machine Learning designer operates.