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Microsoft DP-100 Practice Test - Questions Answers, Page 9

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You use Azure Machine Learning Studio to build a machine learning experiment.

You need to divide data into two distinct datasets.

Which module should you use?

A.
Split Data
A.
Split Data
Answers
B.
Load Trained Model
B.
Load Trained Model
Answers
C.
Assign Data to Clusters
C.
Assign Data to Clusters
Answers
D.
Group Data into Bins
D.
Group Data into Bins
Answers
Suggested answer: D

Explanation:

The Group Data into Bins module supports multiple options for binning data. You can customize how the bin edges are set and how values are apportioned into the bins.

Reference:

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

You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts.

You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription.

You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training. You must minimize data movement.

What should you do?

A.
Create an Azure Data Lake store and move the bird photographs to the store.
A.
Create an Azure Data Lake store and move the bird photographs to the store.
Answers
B.
Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.
B.
Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.
Answers
C.
Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.
C.
Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.
Answers
D.
Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.
D.
Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.
Answers
E.
Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.
E.
Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.
Answers
Suggested answer: D

Explanation:

We recommend creating a datastore for an Azure Blob container. When you create a workspace, an Azure blob container and an Azure file share are automatically registered to the workspace.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

Note: This question-is part of a series of questions that present the same scenario. Each question-in the series contains a unique solution that might meet the stated goals. Some question-sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question-in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are analyzing a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

You need to analyze a full dataset to include all values.

Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.

Does the solution meet the goal?

A.
Yes
A.
Yes
Answers
B.
No
B.
No
Answers
Suggested answer: B

Explanation:

Use the Multiple Imputation by Chained Equations (MICE) method.

Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/ https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

You create an Azure Machine Learning workspace.

You must create a custom role named DataScientist that meets the following requirements:

Role members must not be able to delete the workspace.

Role members must not be able to create, update, or delete compute resource in the workspace.

Role members must not be able to add new users to the workspace.

You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.

The custom role must enforce the restrictions specified by the IT Operations team.

Which JSON code segment should you use?

A.
A.
Answers
B.
B.
Answers
C.
C.
Answers
D.
D.
Answers
Suggested answer: A

Explanation:

The following custom role can do everything in the workspace except for the following actions:

It can't create or update a compute resource.

It can't delete a compute resource.

It can't add, delete, or alter role assignments.

It can't delete the workspace.

To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role. The following example defines a custom role named "Data Scientist Custom" scoped at a specific workspace level:

data_scientist_custom_role.json :

{

"Name": "Data Scientist Custom",

"IsCustom": true,

"Description": "Can run experiment but can't create or delete compute.",

"Actions": ["*"],

"NotActions": [

"Microsoft.MachineLearningServices/workspaces/*/delete",

"Microsoft.MachineLearningServices/workspaces/write",

"Microsoft.MachineLearningServices/workspaces/computes/*/write",

"Microsoft.MachineLearningServices/workspaces/computes/*/delete",

"Microsoft.Authorization/*/write"

],

"AssignableScopes": [

"/subscriptions/<subscription_id>/resourceGroups/<resource_group_name>/providers/Microsoft.MachineLearningServices/workspaces/<workspace_name>"

]

}

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles

Note: This question-is part of a series of questions that present the same scenario. Each question-in the series contains a unique solution that might meet the stated goals. Some question-sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question-in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are a data scientist using Azure Machine Learning Studio.

You need to normalize values to produce an output column into bins to predict a target column.

Solution: Apply an Equal Width with Custom Start and Stop binning mode.

Does the solution meet the goal?

A.
Yes
A.
Yes
Answers
B.
No
B.
No
Answers
Suggested answer: B

Explanation:

Use the Entropy MDL binning mode which has a target column.

Reference:

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

Note: This question-is part of a series of questions that present the same scenario. Each question-in the series contains a unique solution that might meet the stated goals. Some question-sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question-in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are a data scientist using Azure Machine Learning Studio.

You need to normalize values to produce an output column into bins to predict a target column.

Solution: Apply a Quantiles binning mode with a PQuantile normalization.

Does the solution meet the goal?

A.
Yes
A.
Yes
Answers
B.
No
B.
No
Answers
Suggested answer: B

Explanation:

Use the Entropy MDL binning mode which has a target column.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

You are with a time series dataset in Azure Machine Learning Studio.

You need to split your dataset into training and testing subsets by using the Split Data module.

Which splitting mode should you use?

A.
Recommender Split
A.
Recommender Split
Answers
B.
Regular Expression Split
B.
Regular Expression Split
Answers
C.
Relative Expression Split
C.
Relative Expression Split
Answers
D.
Split Rows with the Randomized split parameter set to true
D.
Split Rows with the Randomized split parameter set to true
Answers
Suggested answer: D

Explanation:

Split Rows: Use this 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.

Incorrect Answers:

B: Regular Expression Split: Choose this option when you want to divide your dataset by testing a single column for a value. C: Relative Expression Split: Use this option whenever you want to apply a condition to a number column.

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

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.

A.
login
A.
login
Answers
B.
resource_group
B.
resource_group
Answers
C.
subscription_id
C.
subscription_id
Answers
D.
key
D.
key
Answers
E.
region
E.
region
Answers
Suggested answer: 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

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.

A.
Azure Machine Learning designer
A.
Azure Machine Learning designer
Answers
B.
Azure CLI ml extension v2
B.
Azure CLI ml extension v2
Answers
C.
Azure Machine Learning studio
C.
Azure Machine Learning studio
Answers
D.
BuildContext class in Python SDK v2
D.
BuildContext class in Python SDK v2
Answers
E.
MLCIient class in Python SDK v2
E.
MLCIient class in Python SDK v2
Answers
Suggested answer: A, B, E

Explanation:

Reference:

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

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.

A.
Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.
A.
Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.
Answers
B.
Navigate to Azure Machine Learning studio and create a workspace.
B.
Navigate to Azure Machine Learning studio and create a workspace.
Answers
C.
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 -w and - gparameters for the workspace name and resource group.
C.
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 -w and - gparameters for the workspace name and resource group.
Answers
D.
Navigate to Azure Machine Learning studio and create a workspace.
D.
Navigate to Azure Machine Learning studio and create a workspace.
Answers
E.
Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.
E.
Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.
Answers
Suggested answer: B, C, D

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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