DP-100 Exam Question 31

You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

DP-100 Exam Question 32

You are evaluating a completed binary classification machine learning model.
You need to use the precision as the valuation metric.
Which visualization should you use?
  • DP-100 Exam Question 33

    You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint.
    The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.
    You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.
    You need to configure store1.
    What should you do? To answer, select the appropriate options in the answer area.
    NOTE: Each correct selection is worth one point.

    DP-100 Exam Question 34

    You train and register an Azure Machine Learning model
    You plan to deploy the model to an online endpoint
    You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
    Solution:
    Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.
    Does the solution meet the goal?
  • DP-100 Exam Question 35

    You plan to run a Python script as an Azure Machine Learning experiment.
    The script contains the following code:
    import os, argparse, glob
    from azureml.core import Run
    parser = argparse.ArgumentParser()
    parser.add_argument('--input-data',
    type=str, dest='data_folder')
    args = parser.parse_args()
    data_path = args.data_folder
    file_paths = glob.glob(data_path + "/*.jpg")
    You must specify a file dataset as an input to the script. The dataset consists of multiple large image files and must be streamed directly from its source.
    You need to write code to define a ScriptRunConfig object for the experiment and pass the ds dataset as an argument.
    Which code segment should you use?