Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to use Azure platform tools to detect and analyze food items in smart refrigerators. To provide families with an integrated experience for grocery shopping and cooking, the refrigerators will connect to other smart appliances, such as stoves and microwave ovens, on a LAN.
You plan to build an object recognition model by using the Microsoft Cognitive Toolkit. The object recognition model will receive input from the connected devices and send results to applications.
The training data will be derived from more than 10 TB of images. You will convert the raw images to the sparse format.
End of repeated scenario.
You need to deploy a multiple-service solution that was developed already and published by other users in the Microsoft development community.
What should you use?

• A. the edX Data Science Learning Dashboard
• B. the Data Science Virtual Machine
• C. Cortana Intelligence Gallery
• D. an Azure Machine Learning experiment

Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this scries.
Start of repeated scenario
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
You plan to share the Machine Learning workspace with the other users.
You are evaluating whether to assign the User role or the Owner role to several of the users.
Which three actions can be performed by the users who are assigned the User role? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

• A. Access notebooks.
• B. Create, open, modify, and delete datasets.
• C. Delete users from the workspace.
• D. Invite users to the workspace.
• E. Create, open, modify, and delete web services.
• F. Create, open, modify, and delete experiments.

You have an Azure Machine Learning environment.
You are evaluating whether to use R code or Python.
Which three actions can you perform by using both R code and Python in the Machine Learning environment?
Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

• A. Create visualizations.
• B. Implement feature ranking.
• C. Create an untrained model that can be used with the Train Model module.
• D. Score a training model.
• E. Preprocess, cleanse, and group data.

You are building an Azure Machine Learning experiment.
You need to transform a string column into a label column for a Multiclass Decision Jungle module.
Which module should you use?

• A. Group Categorical Values
• B. Convert to Indicator Values
• C. Select Columns Transform
• D. Edit Metadata