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NEW QUESTION: 1
You have a server named Server1 that runs Windows Server 2016. Server1 is a Hyper-V host that hosts a virtual machine named VM1.
Server1 has three network adapter cards that are connected to virtual switches named vSwitch1, vSwitch2 and vSwitch3.
You configure NIC Teaming on VM1 as shown in the exhibit. (Click the Exhibit button.)

You need to ensure that VM1 will retain access to the network if a physical network adapter card fails on Server1.
What should you do?
A. From Windows PowerShell on Server1, run the Set-VmNetworkAdapterFailoverConfiguration cmdlet.
B. From Windows PowerShell on VM1, run the Set-VmNetworkAdapterTeamMapping cmdlet.
C. From the properties of the NIC team on VM1, add the adapter named Ethernet to the NIC team.
D. From Windows PowerShell on Server1, run Set-VmNetworkAdapter cmdlet.
Answer: D
Explanation:
Explanation
References: https://www.techsupportpk.com/2017/01/nic-teaming-in-hyper-v-on-windows-server-2016.html

NEW QUESTION: 2
Scenario: A Citrix Administrator manages an environment that has a Citrix ADC high availability (HA) pair running on two MPX appliances. The administrator notices that the state of the secondary Citrix ADC is
'Unknown'.
What is causing the secondary state to be 'Unknown'?
A. The administrator made both Citrix ADCs primary.
B. The synchronization on the secondary appliance is disabled.
C. TCP port 22 is disabled between the primary and secondary ADCs.
D. The remote procedure call (RPC) nodes are incorrectly configured.
Answer: D

NEW QUESTION: 3

A. Option E
B. Option A
C. Option D
D. Option B
E. Option C
Answer: A,D

NEW QUESTION: 4
You are working on an Azure Machine Learning experiment that uses four different logistic regression algorithms. You are evaluating the algorithms based on the data in the following table.

Which model produces predictions that are the closest to the actual outcomes?
A. Model 1
B. Model 2
C. Model 4
D. Model 3
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.
Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate- model