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You use Azure Machine Learning designer to load the following datasets into an experiment:
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Join Data module.
Does the solution meet the goal?
You manage an Azure Machine Learning workspace. You design a training job that is configured with a serverless compute. The serverless compute must have a specific instance type and count
You need to configure the serverless compute by using Azure Machine Learning Python SDK v2. What should you do?
You manage an Azure Machine Learning workspace. You plan to import data from Azure Data Lake Storage Gen2. You need to build a URI that represents the storage location. Which protocol should you use?
You manage an Azure Machine Learning workspace.
An MLflow model is already registered. You plan to customize how the deployment does inference. You need to deploy the MLflow model to a batch endpoint for batch inferencing. What should you create first?
You create an Azure Machine Learning workspace named woricspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.
You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.
You need to continue to log metrics and artifacts from your data science code.
What should you do?
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