HOTSPOT Your Azure Machine Learning workspace has a dataset named real_estate_dat a. A sample of the data in the dataset follows. 
You want to use automated machine learning to find the best regression model for predicting the price column. You need to configure an automated machine learning experiment using the Azure Machine Learning SDK. How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. 
Create Azure DevOps project → Create release pipeline → Install ML extension → Create service connection.
I'm a bit unsure on the exact order but from what I'm seeing in practice exams, you need to set up the Azure DevOps project first, then make your pipeline. Installing the Machine Learning extension comes before connecting your workspace so you can deploy models. Can anyone confirm if this order makes sense?
I think the sequence is: Create Azure DevOps project, then set up the release pipeline, install the ML extension, and finally establish the service connection. The service connection needs to be last since it links everything to your workspace after setup. Pretty sure that's right but could be off on step 3 and 4 order. Agree?