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NEW QUESTION: 1
CORRECT TEXT
In order to specify that a Xen virtual machine should start at boot time, under which directory must the VM's configuration file (or a link to it) be placed? (Please specify the directory name with or without the full default path) Your Response
Answer:
Explanation:
/etc/xen/auto auto

NEW QUESTION: 2
When creating a dvPort group, which port binding option assigns a port to a virtual machine when the virtual machine is first powered on?
A. Flexible
B. Ephemeral
C. Dynamic binding
D. Static binding
Answer: C

NEW QUESTION: 3
-- Exhibit - -- Exhibit -

This diagram details the network connectivity from an NSX network and the supporting phy sical network. Locations C and D may be required to process packets with QoS tags.
Based on the diagram, which statement details proper processing of packets if they are QoS tagged?
A. Location B should trust the QoS tags of the external header.
B. Locations C and D will trust the QoS tags of the encapsulated frame when passing packets.
C. Location A will mark the inner header of the encapsulated frame.
D. Location B should trust the QoS tags of the encapsulated frames that are switched.
Answer: A

NEW QUESTION: 4
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.