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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q294-Q299):

NEW QUESTION # 294
You are developing a hands-on workshop to introduce Docker for Windows to attendees.
You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Windows 10 64-bit Professional
B. Kitematic
C. Microsoft Hardware-Assisted Virtualization Detection Tool
D. VirtualBox
E. BIOS-enabled virtualization

Answer: A,E

Explanation:
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example:

E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.
References:
https://docs.docker.com/toolbox/toolbox_install_windows/
https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/

NEW QUESTION # 295

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.

A. Login
B. Key
C. subscription_Id
D. resource_group
E. region

Answer: C,D

Explanation:
Explanation
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

NEW QUESTION # 296
You manage an Azure Machine Learning workspace by using the Azure CLI ml extension v2. You need to define a YAML schema to create a compute cluster. Which schema should you use?

A. https://azuremlschemas.azureedge.net/latest/amlCompute.schemajson
B. https://azuremlschemas.azureedge.net/latest/kubernetesCompute.schema.json
C. https://azuremlschemas.azureedge.net/latest/computdnstarKeichema.json
D. https://azuremlschemas.azureedge.net/latest/vmCompute.schema.json

Answer: A

NEW QUESTION # 297
You create a machine learning model by using the Azure Machine Learning designer. You publish the model as a real-time service on an Azure Kubernetes Service (AKS) inference compute cluster. You make no changes to the deployed endpoint configuration.
You need to provide application developers with the information they need to consume the endpoint.
Which two values should you provide to application developers? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. The run ID of the inference pipeline experiment for the endpoint.
B. The key for the endpoint.
C. The name of the inference pipeline for the endpoint.
D. The URL of the endpoint.
E. The name of the AKS cluster where the endpoint is hosted.

Answer: B,E

NEW QUESTION # 298
You need to define an evaluation strategy for the crowd sentiment models.
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:

Explanation

Scenario:
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
References:
https://en.wikipedia.org/wiki/Nearest_centroid_classifier
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering