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NEW QUESTION: 1
Which one of the following in-the-box ScanJet N6350 software applications allows you to scan slides and negatives with the Transparent Media Adaptor (TMA)?
A. HP Scanning Software
B. Scanner Tools Utility
C. HP Copy Utility
D. Kofax Virtual Rescan Software
Answer: B

NEW QUESTION: 2
このプロセスは、利害関係者の関与レベルの決定、彼らのニーズと関心の理解、および彼らがプロジェクトにどのように影響するか、またはプロジェクトが彼らにどのように影響するかを理解することに関係しています。
A. 通信の制御
B. ステークホルダーエンゲージメントを計画する
C. ステークホルダーエンゲージメントの管理
D. コミュニケーションの管理
Answer: B

NEW QUESTION: 3
Which of the following should be performed to increase the availability of IP telephony by prioritizing traffic?
A. NAT
B. Quality of service
C. NAC
D. Subnetting
Answer: B
Explanation:
Quality of Service (QoS) facilitates the deployment of media-rich applications, such as video conferencing and
Internet Protocol (IP) telephony, without adversely affecting network throughput.
Incorrect Answers:
A. Subnetting is a dividing process used on networks to divide larger groups of hosts into smaller collections.
B. NAT converts the IP addresses of internal systems found in the header of network packets into public IP addresses.
D. The goals of NAC are to prevent/reduce zero-day attacks, enforce security policy throughout the network, and use identities to perform access control.
References:
https://technet.microsoft.com/en-us/library/cc959594.aspx
Stewart, James Michael, CompTIA Security+ Review Guide, Sybex, Indianapolis, 2014, pp. 39, 40

NEW QUESTION: 4
特徴抽出方法を選択する必要があります。
どの方法を使用する必要がありますか？
A. ムードの中央値検定
B. 相互情報
C. 順列機能の重要性
D. ケンドール相関
Answer: D
Explanation:
Explanation
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter ), is a statistic used to measure the ordinal association between two measured quantities.
It is a supported method of the Azure Machine Learning Feature selection.
Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules

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