最好的DP-100測試題庫，由Microsoft權威專家撰寫

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Microsoft DP-100 Exam Syllabus Topics:

Section	Objectives
Design and prepare a machine learning solution	- Manage compute and data assets - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads
Deploy and consume models	- Deploy models to endpoints - Monitor deployed models and endpoints
Optimize and manage models	- Track experiments and manage model lifecycle - Improve model performance
Explore and analyze data	- Perform exploratory data analysis - Ingest and prepare data for modeling
Train machine learning models	- Tune hyperparameters and evaluate models - Train models using Azure Machine Learning

>> DP-100測試題庫 <<

DP-100熱門題庫，DP-100題庫

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最新的 Microsoft Azure DP-100 免費考試真題 (Q348-Q353):

問題 #348
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?

A. Deploy the model to a managed online endpoint.
B. Create the model explanations.
C. Convert the model from the MLflow format to a custom format.
D. Register the model with the workspace.

答案：D

解題說明：
Responsible AI dashboards are linked to your registered models. To view your Responsible AI dashboard, go into your model registry and select the registered model you've generated a Responsible AI dashboard for. Then, select the Responsible AI tab to view a list of generated dashboards.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai- dashboard?view=azureml-api-2

問題 #349
You create an Azure Machine Learning dataset. You use the Azure Machine Learning designer to transform the dataset by using an Execute Python Script component and custom code.
You must upload the script and associated libraries as a script bundle.
You need to configure the Execute Python Script component.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE Each correct selection is worth one point.

答案：

解題說明：

問題 #350
You use an Azure Machine Learning workspace.
You must monitor cost at the endpoint and deployment level.
You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID.
What should you do?

A. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth_mode parameter to configure the authentication type.
B. Deploy the model to a managed online endpoint. During deployment, set the token_auth_mode parameter of the target configuration object to true.
C. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_mode parameter of the target configuration object to true.
D. Deploy the model to a managed online endpoint. During deployment, set the auth_mode parameter to configure the authentication type.

答案：D

解題說明：
Attributes: Diagnostics and Monitoring and Cost
Managed online endpoints (v2):
- Local endpoint debugging possible with Docker and Visual Studio Code
- Advanced metrics and logs analysis with chart/query to compare between deployments
- Cost breakdown down to deployment level
-Azure Monitor and Log Analytics powered (includes key metrics and log tables for endpoints and deployments) ACI or AKS(v1): No easy local debugging
https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints- online?view=azureml-api-2

問題 #351
You use the Azure Machine Learning SDK for Python to create a pipeline that includes the following step:

The output of the step run must be cached and reused on subsequent runs when the source_directory value has not changed.
You need to define the step.
What should you include in the step definition?

A. version
B. data.as_input(name=?
C. allow_reuse
D. hash_paths

答案：C

解題說明：
https://learn.microsoft.com/en-us/python/api/azureml-pipeline-
steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py

問題 #352
You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

答案：

解題說明：

Explanation:
Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Step 2: Add a dataset to the experiment
Step 3: Add a Split Data module to create training and test dataset.
To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.
Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.
Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance

問題 #353
......

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DP-100熱門題庫: https://www.vcesoft.com/DP-100-pdf.html

此外，這些VCESoft DP-100考試題庫的部分內容現在是免費的：https://drive.google.com/open?id=1ELax50B_Myru_PKbhXZkIi696uw4BPNj