Question 11

You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:
iterate all possible combinations of hyperparameters
minimize computing resources required to perform the sweep
You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?
  • Question 12

    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?
  • Question 13

    You have several machine learning models registered in an Azure Machine Learning workspace.
    You must use the Fairlearn dashboard to assess fairness in a selected model.
    Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

    Question 14

    You publish a batch inferencing pipeline that will be used by a business application.
    The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.
    You need to identify the information required in the REST request and returned as a response from the published pipeline.
    Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.
    NOTE: Each correct selection is worth one point.

    Question 15

    You are building an intelligent solution using machine learning models.
    The environment must support the following requirements:
    Data scientists must build notebooks in a cloud environment
    Data scientists must use automatic feature engineering and model building in machine learning pipelines.
    Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.
    Notebooks must be exportable to be version controlled locally.
    You need to create the environment.
    Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.