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NEW QUESTION: 1

A. Event
B. Cisco-av-pair
C. State attribute
D. Class attribute
Answer: B
Explanation:
Reference: https://community.cisco.com/t5/policy-and-access/ise-airespace-acl-wlc-problem/td-p/2110491

NEW QUESTION: 2
VNET1という名前の仮想ネットワークを含むAzureサブスクリプションがあります。 VNET1には、次の表に示すサブネットが含まれます。

各仮想マシンは静的IPアドレスを使用します。
次の要件を満たすために、ネットワークセキュリティグループ（NSG）を作成する必要があります。
*インターネットからVM3、VM4、VM5、およびVM6へのWeb要求を許可します。
* VM1とVM2間のすべての接続を許可します。
* VM1へのリモートデスクトップ接続を許可します。
* VNET1への他のすべてのネットワークトラフィックを防止します。
作成する必要があるNSGの最小数は何ですか？
A. 0
B. 1
C. 2
D. 3
Answer: A
Explanation:
Explanation
Note: A network security group (NSG) contains a list of security rules that allow or deny network traffic to resources connected to Azure Virtual Networks (VNet). NSGs can be associated to subnets, individual VMs (classic), or individual network interfaces (NIC) attached to VMs (Resource Manager).
Each network security group also contains default security rules.
References:
https://docs.microsoft.com/en-us/azure/virtual-network/security-overview#default-security-rules

NEW QUESTION: 3
The Advanced Management POD (AMP) ESXi cluster is performing much slower than expected.
Which command would you use to investigate the slow response in real time?
A. esxtop
B. vmkfstools
C. esxcfg-info
D. vm-support
Answer: A

NEW QUESTION: 4
Which of the following are examples of hyperparameters? (Select 2 answers.)
A. Weights
B. Biases
C. Number of nodes in each hidden layer
D. Number of hidden layers
Answer: C,D
Explanation:
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all.
They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview