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NEW QUESTION: 1
DRAG DROP
Answer:
Explanation:
Create a Reserved IP and associate it with a cloud service (Virtual Machines) Use the following script as a template to create a Reserved IP and then use the Reserved IP to create a cloud service deployment (Virtual Machines).
$ReservedIP = New-AzureReservedIP -ReservedIPName "FirewallIP" -Label "WebAppFirewallIP" - Location "Japan West" New-AzureVMConfig -Name "WebAppVM" -InstanceSize Small -ImageName $images[60].ImageName | Add-AzureProvisioningConfig -Windows -AdminUsername cloudguy -Password Abc123 | New-AzureVM - ServiceName "WebApp" -ReservedIPName $ReservedIP -Location "Japan West"
NEW QUESTION: 2
-- Exhibit-
-- Exhibit -
Refer to the exhibit.
An LTM Specialist configures a virtual server to perform client-side encryption while allowing the server-side traffic to be unencrypted. Application owners report that images are failing to load through the virtual server; however, images load when going directly to the server.
What is the problem with the images loading through the virtual server?
A. The virtual server does not have "SSL Offloading" enabled.
B. Image references are for HTTP objects, not HTTPS.
C. Image references are for HTTPS objects, not HTTP.
D. The virtual server does not have an HTTP profile associated.
Answer: B
NEW QUESTION: 3
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?
A. Fisher Linear Discriminant Analysis.
B. Filter Based Feature Selection
C. Synthetic Minority Oversampling Technique (SMOTE)
D. Permutation Feature Importance
Answer: C
Explanation:
Explanation
Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote