Become Microsoft Certified with updated DP-100 exam questions and correct answers
You create an Azure Machine Learning workspace. The workspace contains a dataset named
sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline
that will prepare data in the dataset and then train and register a model based on the prepared data.
The first stage of the pipeline contains the following code:
You need to identify the location containing the output of the first stage of the script that you can use
as input for the second stage. Which storage location should you use?
You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment. You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets. You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort. What should you do?
You create an Azure Machine Learning workspace. You must create a custom role named DataScientist that meets the following requirements: Role members must not be able to delete the workspace. Role members must not be able to create, update, or delete compute resource in the workspace. Role members must not be able to add new users to the workspace. You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace. The custom role must enforce the restrictions specified by the IT Operations team. Which JSON code segment should you use?

You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder. The experiment fails. You need to troubleshoot the failed experiment. What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
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