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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Certificate Validity Period: | 1 year (renewable through Microsoft certification renewal assessment) |
| Available Languages: | German, English, French, Simplified Chinese, Spanish, Korean, Portuguese (Brazil), Russian, Japanese |
| Exam Format: | Case studies, Multiple response, Hands-on lab tasks, Drag and drop, Multiple choice |
| Exam Duration: | 100-120 |
| Real Exam Qty: | 40-60 |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Passing Score: | 700/1000 |
| Exam Price: | USD 165 (varies by region) |
| Recommended Training: | Azure Machine Learning Documentation Microsoft Learn - DP-100 Learning Path |
| Exam Registration: | Pearson VUE Registration Official Microsoft Certification Page |
| Sample Questions: | Microsoft DP-100 Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers (Pearson VUE) |
| Pre Condition: | No mandatory prerequisites. Recommended familiarity with Python, machine learning concepts, and Azure fundamentals. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-data-scientist/ |
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To nail DP-100, you will need to scrutinize the below-mentioned areas:
The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.
This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.
The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.
Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.
NEW QUESTION # 213
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.
You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298
NEW QUESTION # 214
You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy.
The policy uses a slack_factor set to 01. an evaluation interval set to 1, and an evaluation delay set to b.
You need to evaluate the outcome of the early termination policy
What should you evaluate? To answer, select the appropriate options m the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 215
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: 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
NEW QUESTION # 216
You plan to implement a two-step pipeline by using the Azure Machine Learning SDK for Python.
The pipeline will pass temporary data from the first step to the second step.
You need to identify the class and the corresponding method that should be used in the second step to access temporary data generated by the first step in the pipeline.
Which class and method should you identify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Answer:
Explanation:
Explanation:
NEW QUESTION # 217
You need to build a feature extraction strategy for the local models.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 218
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