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NEW QUESTION: 1
A user is trying to delete an Auto Scaling group from CLI. Which of the below mentioned steps are to be performed by the user?
A. Terminate the Auto Scaling instances with the as-terminate-instance command
B. There is no need to change the capacity. Run the as-delete-group command and it will reset all values to
0
C. Set the minimum size and desired capacity to 0
D. Terminate the instances with the ec2-terminate-instance command
Answer: C
Explanation:
Explanation
If the user wants to delete the Auto Scaling group, the user should manually set the values of the minimum and desired capacity to 0. Otherwise Auto Scaling will not allow for the deletion of the group from CLI. While trying from the AWS console, the user need not set the values to 0 as the Auto Scaling console will automatically do so.
NEW QUESTION: 2
You have a resource group named RG1 that contains the following:
* A virtual network that contains two subnets named Subnet 1 and AzureFirewallSubnet
* An Azure Storage account named contososa1
* An Azure firewall deployed to AzureFirewallSubnet
You need to ensure that contososa1 is accessible from Subnet 1 over the Azure backbone network. What should you do?
A. Remove the Azure firewall-
B. Modify the Firewall and virtual networks settings for contososa1.
C. implement a virtual network service endpoint.
D. Create a stored access policy for contososa1.
Answer: C
Explanation:
Explanation
Virtual Network (VNet) service endpoints extend your virtual network private address space and the identity of your VNet to the Azure services, over a direct connection. Endpoints allow you to secure your critical Azure service resources to only your virtual networks. Traffic from your VNet to the Azure service always remains on the Microsoft Azure backbone network.References:https://docs.microsoft.com/en-us/azure/virtual-network/virtual-network-service-endpoints-o
NEW QUESTION: 3
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps.
Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Explanation:
Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
NEW QUESTION: 4
You configured static mode power management on an EX4300 to provide PoE power to telephone and access point equipment.
Which statement is correct regarding the PoE power budget?
A. Power is budgeted to a port even if no device is connected to the port.
B. Power is budgeted to devices on a first come, first served basis as devices are connected.
C. The power budgeted to ports will adjust to how much power a device actually uses.
D. The power budgeted will be based on the device class connected to each port.
Answer: A