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The DP-100 Certification Exam is an essential tool for professionals who want to demonstrate their expertise in designing and implementing data science solutions on Azure. Designing and Implementing a Data Science Solution on Azure certification exam is ideal for data scientists, software developers, and IT professionals who want to improve their knowledge and skills in Azure data science solutions. DP-100 exam is also suitable for professionals who want to advance their careers in data science.

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DP-100 Exam Dumps: Designing and Implementing a Data Science Solution on Azure & DP-100 Training Materials & DP-100 Dumps Torrent

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q75-Q80):

NEW QUESTION # 75
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?

Answer: D

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

NEW QUESTION # 76
You are using Azure Machine Learning to train machine learning models. You need a compute target on which to remotely run the training script. You run the following Python code:

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

NEW QUESTION # 77
You create an Azure Machine learning workspace and load a Python training script named tram.py in the src subfolder. The dataset used to train your model is available locally. You run the following script to tram the model:

instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

NEW QUESTION # 78
You plan to preprocess text from CSV files. You load the Azure Machine Learning Studio default stop words list.
You need to configure the Preprocess Text module to meet the following requirements:
Ensure that multiple related words from a single canonical form.
Remove pipe characters from text.
Remove words to optimize information retrieval.
Which three options should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

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

NEW QUESTION # 79
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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?

A. No
B. Yes

Answer: A

Explanation:
Explanation
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn

NEW QUESTION # 80
......

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100% PASS 2025 MICROSOFT HIGH-QUALITY DP-100 AUTHORIZED PDF

100% Pass 2025 Microsoft High-quality DP-100 Authorized Pdf