Microsoft Azure AI Fundamentals (AI-900) Practice Exam

In which way does Azure ML designer cater to collaborative work?

1. By exclusively allowing single-user access

2. By enabling saving and sharing of pipeline drafts

3. By requiring all users to code

4. By focusing only on visual data analysis

The correct answer is: By enabling saving and sharing of pipeline drafts

Azure ML designer facilitates collaborative work by enabling users to save and share pipeline drafts. This capability allows multiple team members to contribute to the same machine learning project by working on pipeline drafts collaboratively. They can easily share these drafts with others, enabling feedback, revision, and collective progress toward the final model. This functionality is essential in environments where teamwork and the integration of diverse skill sets are crucial for developing robust machine learning solutions. The other options would not support collaborative work effectively. Allowing only single-user access would hinder teamwork, as no one else could contribute to or access the work. Requiring all users to code limits participation to those with programming knowledge, which can exclude valuable contributions from individuals with different expertise. Focusing solely on visual data analysis could also restrict the use of data science techniques and methods, which might be necessary for comprehensive project development and reflection of diverse perspectives in a team setting.