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2025 Efficient Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure Reliable Dumps Book

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Microsoft DP-100 certification exam is a comprehensive test that covers a wide range of topics related to data science and machine learning. It consists of various modules that test your knowledge of data exploration, data preprocessing, feature engineering, model training, and deployment. DP-100 exam also evaluates your understanding of machine learning algorithms, statistical modeling, and data visualization techniques. By passing the DP-100 exam, you can demonstrate your proficiency in all these areas and prove your ability to design and implement effective data science solutions on Azure.

Microsoft DP-100 Certification Exam is a comprehensive test designed to assess the candidate's knowledge and expertise in designing and implementing data science solutions on the Azure platform. DP-100 Exam is designed for data professionals, data scientists, and developers who want to expand their skill set and validate their knowledge of Azure data services. The DP-100 exam covers a wide range of topics, including data exploration and preparation, modeling, deployment, and monitoring.

Becoming certified in Microsoft DP-100 demonstrates to employers and clients that you have the skills and knowledge necessary to design and implement data science solutions on Azure. It also shows that you are committed to continuing your education and staying up-to-date with the latest technologies and best practices in the field.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q406-Q411):

NEW QUESTION # 406
You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements:
* Provide near real-time inferencing.
* Enable endpoint and deployment level cost estimates.
* Support logging to Azure Log Analytics.
You need to configure the deployment solution.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 407
You have a dataset contains 2,000 rows. You arc building a machine learning classification model by using Azure Machine Learning Studio. You add a Partition and Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
* Divide the data into subsets.
* Assign the rows into folds using a round-robin method.
* Allow rows in the dataset to be reused.
How should you configure the module? To answer select the appropriate Options m the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 408
You need to define a process for penalty event detection.
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:

NEW QUESTION # 409
You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and calculates its accuracy metric.
The model will be retrained each month as new data is available.
You must register the model for use in a batch inference pipeline.
You need to register the model and ensure that the models created by subsequent retraining experiments are registered only if their accuracy is higher than the currently registered model.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Specify a different name for the model each time you register it.
B. Register the model with the same name each time regardless of accuracy, and always use the latest version of the model in the batch inferencing pipeline.
C. Specify the model framework version when registering the model, and only register subsequent models if this value is higher.
D. Specify a tag named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the tag value of the currently registered model.
E. Specify a property named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy property value of the currently registered model.

Answer: C,D

Explanation:
Explanation
E: Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric.
Reference:
https://notebooks.azure.com/xavierheriat/projects/azureml-getting-started/html/how-to-use-azureml/deployment/

NEW QUESTION # 410
You train and register a model by using the Azure Machine Learning SDK on a local workstation. Python 3.6 and Visual Studio Code are installed on the workstation.
When you try to deploy the model into production as an Azure Kubernetes Service (AKS)-based web service, you experience an error in the scoring script that causes deployment to fail.
You need to debug the service on the local workstation before deploying the service to production.
Which four 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:

1 - Install Docker on the workstation
2 - Create an AksWebservice deployment configuration and deploy the model to it
3 - Create a LocalWebservice deployment configuration for the service and deploy the model to it
4 - Debug and modify the scoring script as necessary. Use the reload() method of the service after each modification.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment-local

NEW QUESTION # 411
......

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