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NEW QUESTION: 1
注この質問は、同じ見方を示す一連の質問の一部です。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
West US AzureリージョンでホストされているVNet1という名前の仮想ネットワークを管理します。
VNet1は、Windows Serverを実行するVM1およびVM2という名前の2つの仮想マシンをホストします。
VM1からVM2へのすべてのネットワークトラフィックを3時間検査する必要があります。
解決策：パフォーマンスモニターから、データコレクターセット（DCS）を作成します。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
Network performance monitor allows you to monitor connectivity and latencies across hybrid network architectures, Expressroute circuits, and service/application endpoints.
With an data collector set we can count specified network traffic, but we cannot inspect it.
For this we would need a network watcher Packet Capture.
References:
https://docs.microsoft.com/en-us/azure/network-watcher/network-watcher-monitoring-overview
https://docs.microsoft.com/en-us/azure/azure-monitor/insights/network-performance-monitor References:
https://docs.microsoft.com/en-us/azure/network-watcher/network-watcher-monitoring-overview

NEW QUESTION: 2

A. Option B
B. Option A
C. Option D
D. Option C
Answer: A
Explanation:
See link for an article on both Routing and Remote Access server role and the DHCP default gateway option. http://blogs.technet.com/b/rrasblog/archive/2009/03/25/remote-access-deployment-part-2configuringrras-asa-vpn-server.aspx

NEW QUESTION: 3
When an Arista switch boots with no startup-config and ZTP enabled, before ZTP receives an IP address, in what state are the front-panel interfaces?
A. All interfaces are switch ports (switchport).
B. All interfaces are routed ports (no switchport).
C. All interfaces are disabled.
D. All interfaces are shut down.
Answer: D

NEW QUESTION: 4
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote