最高のDP-100資格受験料 & 合格スムーズDP-100無料サンプル | 高品質なDP-100資料的中率そんなに多くの人はMicrosoft DP-100試験に合格できるのに興味がわきますか。人に引けをとりたくないあなたはMicrosoft DP-100資格認定を取得したいですか。ここで、彼らはDP-100試験にうまく合格できる秘訣は我々社の提供する質高いMicrosoft DP-100問題集を利用したことだと教えます。弊社のMicrosoft DP-100問題集を通して復習してから、真実的に自分の能力の向上を感じ、DP-100資格認定を受け取ります。
この試験は、データの探索と準備、モデリング、機械学習モデルの展開とメンテナンスなどの重要な概念の理解を試験するよう設計されています。Azure Machine Learning、Azure Databricks、Azure HDInsightなどのAzureデータサービスに深い理解が必要です。さらに、候補者はPythonやRなどのプログラミング言語についても良好な理解を持っていることが期待されています。 Microsoft Designing and Implementing a Data Science Solution on Azure 認定 DP-100 試験問題 (Q337-Q342):質問 # 337
You are using an Azure Machine Learning workspace. You set up an environment for model testing and an environment for production.
The compute target for testing must minimize cost and deployment efforts. The compute target for production must provide fast response time, autoscaling of the deployed service, and support real-time inferencing.
You need to configure compute targets for model testing and production.
Which compute targets should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point. 正解：
解説：
Explanation:
Box 1: Local web service
The Local web service compute target is used for testing/debugging. Use it for limited testing and troubleshooting. Hardware acceleration depends on use of libraries in the local system.
Box 2: Azure Kubernetes Service (AKS)
Azure Kubernetes Service (AKS) is used for Real-time inference.
Recommended for production workloads.
Use it for high-scale production deployments. Provides fast response time and autoscaling of the deployed service Reference: https://docs.microsoft.com/en-us ... cept-compute-target
質問 # 338
You need to define a modeling strategy for ad response.
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. 正解：
解説：
Explanation
Step 1: Implement a K-Means Clustering model
Step 2: Use the cluster as a feature in a Decision jungle model.
Decision jungles are non-parametric models, which can represent non-linear decision boundaries.
Step 3: Use the raw score as a feature in a Score Matchbox Recommender model The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system.
Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with item preferences.
Scenario:
Ad response rated declined.
Ad response models must be trained at the beginning of each event and applied during the sporting event.
Market segmentation models must optimize for similar ad response history.
Ad response models must support non-linear boundaries of features.
References: https://docs.microsoft.com/en-us ... ass-decision-jungle https://docs.microsoft.com/en-us ... matchbox-recommende
質問 # 339
You define a datastore named ml-data for an Azure Storage blob container. In the container, you have a folder named train that contains a file named data.csv. You plan to use the file to train a model by using the Azure Machine Learning SDK.
You plan to train the model by using the Azure Machine Learning SDK to run an experiment on local compute.
You define a DataReference object by running the following code:
You need to load the training data.
Which code segment should you use?
質問 # 340
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.
B.
C.
D.
正解：B
解説：
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 ... how-to-assign-roles
質問 # 341
You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.
You submit the experiment with the following code:
You need to create Python code that returns the best model that is generated by the automated machine learning task. Which code segment should you use?
