Explanation:
Based on the community discussion and Azure ML documentation, the Basic edition workspace supports: A) Creating Compute Instances for running code in Jupyter notebooks, which is a core development feature; C) Using the designer with drag-and-drop modules for model training, available in Basic edition; and E) Using the Automated ML UI for automated model training, which is also supported. Option B (creating AKS inference clusters) is not supported in Basic edition as it requires enterprise features for production deployment. Option D (creating versioned tabular datasets) is supported in Azure ML, but the community consensus and upvoted comments (e.g., NullVoider_0 with 12 upvotes) clarify that ACE are the correct answers, aligning with the 80% community selection and the question's focus on Basic edition capabilities.
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You are planning to provision an Azure Machine Learning Basic edition workspace for a data science project. You need to identify which tasks can be performed in the workspace. Which three tasks can be performed? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
A
Create a Compute Instance and use it to run code in Jupyter notebooks.
B
Create an Azure Kubernetes Service (AKS) inference cluster.
C
Use the designer to train a model by dragging and dropping pre-defined modules.
D
Create a tabular dataset that supports versioning.
E
Use the Automated Machine Learning user interface to train a model.