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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Available Languages: | Korean, Spanish, Japanese, English, German, Portuguese (Brazil), Russian, Simplified Chinese, French |
| Exam Duration: | 100-120 |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Real Exam Qty: | 40-60 |
| Exam Price: | USD 165 (varies by region) |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | 1 year (renewable through Microsoft certification renewal assessment) |
| Exam Format: | Multiple response, Multiple choice, Case studies, Drag and drop, Hands-on lab tasks |
| Recommended Training: | Microsoft Learn - DP-100 Learning Path Azure Machine Learning Documentation |
| 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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Microsoft DP-100 exam is designed for data scientists who want to demonstrate their skills in designing and implementing data science solutions on Azure. DP-100 exam focuses on evaluating the candidate's knowledge and proficiency in various areas such as data exploration and preparation, modeling, deployment, and monitoring. DP-100 Exam also includes topics related to machine learning, data engineering, and data visualization.
NEW QUESTION # 474
You create an Azure Machine Learning workspace.
You must create a custom role named 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:
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.MachineLearni
]
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles
NEW QUESTION # 475
You create a classification model with a dataset that contains 100 samples with Class A and
10,000 samples with Class B
The variation of Class B is very high.
You need to resolve imbalances.
Which method should you use?
Answer: D
NEW QUESTION # 476
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.
Answer:
Explanation:
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
NEW QUESTION # 477
You manage an Azure Machine Learning workspace named workspaces
You plan to create a registry named registry01 with the help of the following registry.yml (line numbers are used for reference only):
You need to use Azure Machine Learning Python SDK v2 with Python 3.10 in a notebook to interact with workspace1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 478
You need to identify the methods for dividing the data according to the testing requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:
Explanation:
Scenario: Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.
Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the dat a. 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.
Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:
Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample
NEW QUESTION # 479
......
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