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NEW QUESTION: 1

Answer:
Explanation:
See explanation below.
Explanation
Virtual network peering enables you to seamlessly connect two Azure virtual networks. Once peered, the virtual networks appear as one, for connectivity purposes.
Peer virtual networks
Step 1. In the Search box at the top of the Azure portal, begin typing VNET01-USEA2. When VNET01-USEA2 appears in the search results, select it.
Step 2. Select Peerings, under SETTINGS, and then select + Add, as shown in the following picture:

Step 3. Enter, or select, the following information, accept the defaults for the remaining settings, and then select OK.
Name: myVirtualNetwork1-myVirtualNetwork2 (for example)
Subscription: elect your subscription.
Virtual network: VNET01-USWE2 - To select the VNET01-USWE2 virtual network, select Virtual network, then select VNET01-USWE2. You can select a virtual network in the same region or in a different region.
Now we need to repeat steps 1-3 for the other network VNET01-USWE2:
Step 4. In the Search box at the top of the Azure portal, begin typing VNET01- USEA2. When VNET01- USEA2 appears in the search results, select it.
Step 5. Select Peerings, under SETTINGS, and then select + Add.
References:
https://docs.microsoft.com/en-us/azure/virtual-network/tutorial-connect-virtual-networks-portal

NEW QUESTION: 2
Which ICMP message type is sent by the router if an IP packet TTL field reaches zero?
A. Destination Unreachable
B. ICMP Time-Exceeded
C. Echo Reply
D. Echo Request
Answer: B

NEW QUESTION: 3
Azure Machine Learningデザイナーを使用して実験を構築しています。
データセットをトレーニングセットとテストセットに分割します。 Two-Class Boosted Decision Treeをアルゴリズムとして選択します。
モデルの曲線下面積（AUC）を決定する必要があります。
どの3つのモジュールを順番に使用する必要がありますか？回答するには、適切なモジュールをモジュールのリストから回答領域に移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation:
Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:

Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.

Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.