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NEW QUESTION: 1
Refer to the exhibit.

Which statement about the output of the show flow-sampler command is true?
A. The sampler matched 10 packets, one packet every 100 seconds.
B. The sampler matched 10 packets, each packet randomly chosen from every group of 100 packets.
C. The sampler matched 10 packets, each one randomly chosen from every 100-second interval.
D. The sampler matched 10 packets, one packet every 100 packets.
Answer: B
Explanation:
Explanation/Reference:
Explanation:
The sampling mode determines the algorithm that selects a subset of traffic for NetFlow processing. In the random sampling mode that Random Sampled NetFlow uses, incoming packets are randomly selected so that one out of each n sequential packets is selected on average for NetFlow processing. For example, if you set the sampling rate to 1 out of 100 packets, then NetFlow might sample the 5th, 120th, 199th, 302nd, and so on packets. This sample configuration provides NetFlow data on 1 percent of total traffic. The n value is a parameter from 1 to 65535 packets that you can configure.
Table 2 show flow-sampler Field Descriptions
Field Description
Sampler Name of the flow sampler
id Unique ID of the flow sampler
packets matched Number of packets matched for the flow sampler mode Flow sampling mode sampling interval is Flow sampling interval (in packets) Reference:
http://www.cisco.com/c/en/us/td/docs/ios/12_0s/feature/guide/nfstatsa.html#wp1084291

NEW QUESTION: 2
Which two commands are valid URL filtering commands? (Choose two.)
A. url-server (DMZ) vendor n2h2 host 10.0.1.1
B. url-server (DMZ) vendor web host 10.0.1.1
C. url-server (DMZ) vendor smartfilter host 10.0.1.1
D. url-server (DMZ) vendor CISCO host 10.0.1.1
E. url-server (DMZ) vendor url-filter host 10.0.1.1
Answer: A,C

NEW QUESTION: 3
Of the two types of energy transfer, which type would a Motor Vehicle crash (MVC) with frontal impact, non-restrained driver with bent steering wheel and spidered windshield?
A. Blunt
B. Decelerating
C. High velocity penetrating
D. Low velocity penetrating
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Blunt trauma is just not getting stabbed or shot. Blunt trauma is any injury which is caused by something sticking the human body and having energy transferred to the human body. Blunt trauma can be caused by decelerating injury like a front end impact of a car against a tree.

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.