DP-100 Exam Question 146

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?
  • DP-100 Exam Question 147

    You manage an Azure Machine Learning workspace.
    You must define the execution environments for your jobs and encapsulate the dependencies for your code.
    You need to configure the environment from a Docker build context.
    How should you complete the rode segment? To answer, select the appropriate option in the answer area.
    NOTE: Each correct selection is worth one point.

    DP-100 Exam Question 148

    You plan to implement an Azure Machine Learning solution. You have the following requirements:
    * Run a Jupyter notebook to interactively tram a machine learning model.
    * Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming.
    You need to select a development technique for each requirement
    Which development technique should you use? To answer, select the appropriate options in the answer area.
    NOTE: Each correct selection is worth one point.

    DP-100 Exam Question 149

    You create and register a model in an Azure Machine Learning workspace.
    You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data using the model. You must specify a value for the ParallelRunConfig compute_target setting of the pipeline step.
    You need to create the compute target.
    Which class should you use?
  • DP-100 Exam Question 150

    You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).
    The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
    You need to configure the module.
    Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
    NOTE: Each correct selection is worth one point.