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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q194-Q199):

NEW QUESTION # 194
You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure.
You need to access the DSVMs.
Which utilities should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 195
You use Azure Machine Learning designer to create a real-time service endpoint. You have a single Azure Machine Learning service compute resource. You train the model and prepare the real-time pipeline for deployment You need to publish the inference pipeline as a web service. Which compute type should you use?

  • A. Azure Kubernetes Services
  • B. HDInsight
  • C. a new Machine Learning Compute resource
  • D. Azure Databricks
  • E. the existing Machine Learning Compute resource

Answer: E

NEW QUESTION # 196
You are developing a deep learning model by using TensorFlow. You plan to run the model training workload on an Azure Machine Learning Compute Instance.
You must use CUDA-based model training.
You need to provision the Compute Instance.
Which two virtual machines sizes can you use? To answer, select the appropriate virtual machine sizes in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html

NEW QUESTION # 197
You deploy a model as an Azure Machine Learning real-time web service using the following code.

The deployment fails.
You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.
Which code segment should you run?

  • A. service.serialize()
  • B. service.update_deployment_state()
  • C. service.state
  • D. service.get_logs()

Answer: D

Explanation:
You can print out detailed Docker engine log messages from the service object. You can view the log for ACI, AKS, and Local deployments. The following example demonstrates how to print the logs.
# if you already have the service object handy
print(service.get_logs())
# if you only know the name of the service (note there might be multiple services with the same name but different version number) print(ws.webservices[‘mysvc’].get_logs()) Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

NEW QUESTION # 198
You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).
The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 199
……

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