Microsoft DP-100受験対策解説集、DP-100試験時間

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我々Tech4Examが数年以来商品の開発をしている目的はIT業界でよく発展したい人にMicrosoftのDP-100試験に合格させることです。MicrosoftのDP-100試験のための資料がたくさんありますが、Tech4Examの提供するのは一番信頼できます。我々の提供するソフトを利用する人のほとんどは順調にMicrosoftのDP-100試験に合格しました。その中の一部は暇な時間だけでMicrosoftのDP-100試験を準備します。

Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deploy and retrain models10-15%- Monitor deployed models
  • 1. Monitor model performance
  • 2. Track data drift
- Implement retraining pipelines
  • 1. Manage ML pipelines
  • 2. Create scheduled retraining workflows
Topic 2: Design and prepare a machine learning solution20-25%- Design an Azure Machine Learning workspace
  • 1. Manage compute resources
  • 2. Configure security and access
  • 3. Configure workspace resources
- Prepare development environments
  • 1. Configure environments
  • 2. Use SDKs and notebooks
Topic 3: Prepare a model for deployment20-25%- Manage deployment assets
  • 1. Create inference configurations
  • 2. Register models
- Deploy machine learning models
  • 1. Deploy batch inference pipelines
  • 2. Deploy real-time inference endpoints
Topic 4: Explore data and train models35-40%- Run experiments and train models
  • 1. Track experiments
  • 2. Use automated machine learning
  • 3. Perform hyperparameter tuning
- Prepare data for modeling
  • 1. Ingest and transform data
  • 2. Manage datasets and datastores
- Optimize model performance
  • 1. Improve accuracy and performance
  • 2. Evaluate models

>> Microsoft DP-100受験対策解説集 <<

無駄なく効率よく短時間で Designing and Implementing a Data Science Solution on Azure 合格レベルに到達

当社はTech4Exam、世界中のDP-100試験トレントコンパイル部門の販売およびアフターサービスを提供する多国籍企業です。 さらに、当社はこの分野で一流の企業になりました。そのため、関連するDP-100認定を取得するために試験の準備をしている場合、当社がまとめたDP-100のMicrosoft試験問題はあなたの堅実なものです。 選択。 当社の世界中のすべての従業員は、お客様がDP-100試験に合格するための電子的なDP-100試験トレントの最高のグローバルサプライヤになるという共通の使命の下でDesigning and Implementing a Data Science Solution on Azure運営されています。

Microsoft Designing and Implementing a Data Science Solution on Azure 認定 DP-100 試験問題 (Q230-Q235):

質問 # 230
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
Text, application Description automatically generated

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/azure/machine-learning/concept-compute-target


質問 # 231

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.

正解：C、E

解説：
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


質問 # 232
You have an Azure Machine learning workspace. The workspace contains a dataset with data in a tabular form.
You plan to use the Azure Machine Learning SDK for Python vl to create a control script that will load the dataset into a pandas dataframe in preparation for model training The script will accept a parameter designating the dataset You need to complete the script.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:


質問 # 233
You are creating a machine learning model that can predict the species of a penguin from its measurements. You have a file that contains measurements for free species of penguin in comma delimited format.
The model must be optimized for area under the received operating characteristic curve performance metric averaged for each class.
You need to use the Automated Machine Learning user interface in Azure Machine Learning studio to run an experiment and find the best performing model.
Which five 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 collect order.

正解：

解説：

1 - Create and select a new dataset...
2 - Select the Classification task type.
3 - Set the Primary metric configuration setting to Accuracy.
4 - Configure the automated machine learning...
5 - Run the automated machine learning experiment and review the results.


質問 # 234
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible Al dashboard to assess the model.
You need to use the Azure Machine Learning studio Ul to generate the Responsible A dashboard.
What should you do first?

正解：A


質問 # 235
......

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DP-100試験時間: https://www.tech4exam.com/DP-100-pass-shiken.html

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