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NEW QUESTION: 1
When attempting to remove a host from a vSphere Distributed Switch (vDS), an administrator observes the error message:
The resource '16' is in use
What are two reasons why this error would be displayed? (Choose two.)
A. VMkernel network adapters on the vDS are in use.
B. There is active network traffic on the vDS.
C. Network I/O Control has been configured on the vDS.
D. Virtual machine network adapters are connected to the vDS.
Answer: A,D
Explanation:
Explanation/Reference:
Explanation: Cause
You cannot remove the host from the distributed switch or delete the host proxy switch because of the following reasons.
There are VMkernel adapters on the switch that are in use.

There are virtual machine network adapters connected to the switch.

Reference: https://pubs.vmware.com/vsphere-55/index.jsp?topic=%
2Fcom.vmware.vsphere.troubleshooting.doc%2FGUID-038AC93F-D710-48ED-8E3B-258A23FB2930.html

NEW QUESTION: 2
How can you get a neural network to learn about relationships between categories in a categorical feature?
A. Create a hash bucket
B. Create a one-hot column
C. Create an embedding column
D. Create a multi-hot column
Answer: C
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column.
The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too. Reference: https:// cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and- deep-model.html

NEW QUESTION: 3
Your network contains an Active Directory domain named contoso.com. The domain contains two servers
named Server1 and Server2 that run Windows Server 2016. Server1 and Server2 have multiple local disks
attached.
You need to create a storage pool by using Storage Spaces Direct.
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

References:
https://docs.microsoft.com/en-us/windows-server/storage/storage-spaces/deploy-storage-spaces-direct