Microsoft Azure DP-100 Book

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Question 32

You create an Azure Machine Learning workspace and a new Azure DevOps organization. You register a model in the workspace and deploy the model to the target environment.

All new versions of the model registered in the workspace must automatically be deployed to the target environment.

You need to configure Azure Pipelines to deploy the model.

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.

Options:

Question 33

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?

Options:

Random sweep

Sweep clustering

Entire grid

Random grid

Random seed

Question 34

You have a dataset that contains records of patients tested for diabetes. The dataset includes the patient s age.

You plan to create an analysis that will report the mean age value from the differentially private data derived from the dataset-

You need to identify the epsilon value to use in the analysis that minimizes the risk of exposing the actual data.

Which epsilon value should you use?

Options:

-1.5

-0.5

0.5

1.5

Question 35

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?

Options:

Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.

Always run the experiment with an Estimator by using the default packages.

Do not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.

Create a config. yaml file defining the conda packages that are required and save the file in the experiment folder.

Create a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.