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Free DP-100 Exam Braindumps (page: 25)

Page 24 of 127
PDF with 562 Questions

You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:
-Minimum nodes: 2
-Maximum nodes: 4
You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:
-Minimum nodes: 0
-Maximum nodes: 8
You need to reconfigure the compute resource.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  1. Use the Azure Machine Learning studio.
  2. Run the update method of the AmlCompute class in the Python SDK.
  3. Use the Azure portal.
  4. Use the Azure Machine Learning designer.
  5. Run the refresh_state() method of the BatchCompute class in the Python SDK.

Answer(s): A,B,C

Explanation:

A: You can manage assets and resources in the Azure Machine Learning studio.
B: The update(min_nodes=None, max_nodes=None, idle_seconds_before_scaledown=None) of the AmlCompute class updates the ScaleSettings for this
AmlCompute target.
C: To change the nodes in the cluster, use the UI for your cluster in the Azure portal.


Reference:

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)



HOTSPOT (Drag and Drop is not supported)
You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and
Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
-Divide the data into subsets
-Assign the rows into folds using a round-robin method
-Allow rows in the dataset to be reused
How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Use the Split data into partitions option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
1. Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.
2. For Partition or sample mode, select Assign to Folds.
3. Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.
4. If you do not use replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold.
5. Randomized split: Select this option if you want rows to be randomly assigned to folds.
If you do not select this option, rows are assigned to folds using the round-robin method.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample



You create a new Azure subscription. No resources are provisioned in the subscription.
You need to create an Azure Machine Learning workspace.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  1. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, and resource_group parameters.
  2. Navigate to Azure Machine Learning studio and create a workspace.
  3. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with --name and --location parameters, and then the az ml workspace create function, specifying ג€"w and ג€"g parameters for the workspace name and resource group.
  4. Navigate to Azure Machine Learning studio and create a workspace.
  5. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.

Answer(s): A,B,C

Explanation:

B: You can create a workspace in the Azure Machine Learning studio
C: You can create a workspace for Azure Machine Learning with Azure CLI
Install the machine learning extension.
Create a resource group: az group create --name <resource-group-name> --location <location>
To create a new workspace where the services are automatically created, use the following command: az ml workspace create -w <workspace-name> -g
<resource-group-name>
D: You can create and manage Azure Machine Learning workspaces in the Azure portal.
1. Sign in to the Azure portal by using the credentials for your Azure subscription.
2. In the upper-left corner of Azure portal, select + Create a resource.
3. Use the search bar to find Machine Learning.
4. Select Machine Learning.
5. In the Machine Learning pane, select Create to begin.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-cli https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace



HOTSPOT (Drag and Drop is not supported)
You create an Azure Machine Learning workspace and set up a development environment. You plan to train a deep neural network (DNN) by using the
Tensorflow framework and by using estimators to submit training scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: Tensorflow
TensorFlow represents an estimator for training in TensorFlow experiments.
Box 2: 12 vCPU, 112 GB memory..,2 GPU,..
Use GPUs for the deep neural network.


Reference:

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn






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