DP-100 Exam Question 46

You are creating a binary classification by using a two-class logistic regression model.
You need to evaluate the model results for imbalance.
Which evaluation metric should you use?
  • DP-100 Exam Question 47

    An organization uses Azure Machine Learning service and wants to expand their use of machine learning.
    You have the following compute environments. The organization does not want to create another compute environment.

    You need to determine which compute environment to use for the following scenarios.
    Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
    NOTE: Each correct selection is worth one point.

    DP-100 Exam Question 48

    You create a binary classification model using Azure Machine Learning Studio.
    You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.
    You need to create the required business metrics.
    How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area.
    NOTE: Each correct selection is worth one point.

    DP-100 Exam Question 49

    You are creating a new Azure Machine Learning pipeline using the designer.
    The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file.
    You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.
    Which module should you add to the pipeline in Designer?
  • DP-100 Exam Question 50

    You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.
    You must deploy the model as an Azure Machine Learning real-time web service using the Model.deploy method in the Azure Machine Learning SDK. The deployed web service must return real-time predictions of fraud based on transaction data input.
    You need to create the script that is specified as the entry_script parameter for the InferenceConfig class used to deploy the model.
    What should the entry script do?