DP-100 Exam Questions and Answers | Exam-Dumps (Page: 19)

QUESTION: 91 Exam Topic: Define and prepare the development environment

You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. You want to use the laptop to connect to the workspace and run experiments.
You create the following config.json file.
{
"workspace_name" : "ml-workspace"
}
You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.
You need to configure the config.json file to connect to the workspace from the Python environment.
Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

login
resource_group
subscription_id
key
region

Answer(s): B,C

Explanation:

To use the same workspace in multiple environments, create a JSON configuration file. The configuration file saves your subscription (subscription_id), resource
(resource_group), and workspace name so that it can be easily loaded.
The following sample shows how to create a workspace.
from azureml.core import Workspace
ws = Workspace.create(name='myworkspace',
subscription_id='<azure-subscription-id>',
resource_group='myresourcegroup',
create_resource_group=True,
location='eastus2'
)

Reference:

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

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QUESTION: 92 Exam Topic: Define and prepare the development environment

HOTSPOT (Drag and Drop is not supported)
You are performing a classification task in Azure Machine Learning Studio.
You must prepare balanced testing and training samples based on a provided data set.
You need to split the data with a 0.75:0.25 ratio.
Which value should you use for each parameter? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

See Explanation section for answer.

Answer(s): A

Explanation:

Box 1: Split rows
Use the Split Rows option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50.
You can also randomize the selection of rows in each group, and use stratified sampling. In stratified sampling, you must select a single column of data for which you want values to be apportioned equally among the two result datasets.
Box 2: 0.75
If you specify a number as a percentage, or if you use a string that contains the "%" character, the value is interpreted as a percentage. All percentage values must be within the range (0, 100), not including the values 0 and 100.
Box 3: Yes
To ensure splits are balanced.
Box 4: No
If you use the option for a stratified split, the output datasets can be further divided by subgroups, by selecting a strata column.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

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QUESTION: 93 Exam Topic: Define and prepare the development environment

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.

Use the Azure Machine Learning studio.
Run the update method of the AmlCompute class in the Python SDK.
Use the Azure portal.
Use the Azure Machine Learning designer.
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)

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QUESTION: 94 Exam Topic: Define and prepare the development environment

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:

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

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QUESTION: 95 Exam Topic: Define and prepare the development environment

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.

Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, and resource_group parameters.
Navigate to Azure Machine Learning studio and create a workspace.
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.
Navigate to Azure Machine Learning studio and create a workspace.
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

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Microsoft DP-100 Exam (page: 19) Microsoft Designing and Implementing a Data Science Solution on Azure Updated on: 15-Feb-2026

QUESTION: 91 Exam Topic: Define and prepare the development environment

Explanation:

Reference:

QUESTION: 92 Exam Topic: Define and prepare the development environment

Explanation:

Reference:

QUESTION: 93 Exam Topic: Define and prepare the development environment

Explanation:

Reference:

QUESTION: 94 Exam Topic: Define and prepare the development environment

Explanation:

Reference:

QUESTION: 95 Exam Topic: Define and prepare the development environment

Explanation:

Reference:

Microsoft DP-100 Exam (page: 19)
Microsoft Designing and Implementing a Data Science Solution on Azure
Updated on: 15-Feb-2026