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NEW QUESTION: 1
An engineer is tuning RRM parameters to improve client connectivity. Which channel band results in the best 802.11n
client compatibility?
A. UNII-2
B. UNII-2e
C. UNII-3
D. UNII
E. UNII-1
Answer: E
Explanation:
802.11n operates on the same channel as 802.11a. For better compatibility with 802.11n clients, it is recommended
to stay on lower channels (UNII-1 band).
Reference:
http://www.cisco.com/c/en/us/support/docs/wireless/4400-series-wireless-lan-controllers/108184config-802-11n-
wlc.html

NEW QUESTION: 2
The account of a user named User1 is assigned three security rotes. The security roles are defined as shown m the following tables.

You need to identify which permissions to the account entity are assigned to User1. What should you identify?
A. * Read User
* Write Business Unit
* Delete User
B. * Read Business Unit
* Write None
* Delete None
C. * Read Organization
* Write Business Unit
* Delete User
D. * Read Organization
* Write User
* Delete None
E. * Read User
* Write None
* Delete None
Answer: C

NEW QUESTION: 3
Your network contains a System Center 2012 R2 Configuration Manager Service Pack 1
(SP1) environment.
You deploy a Microsoft Office 2010 package to all client computers by using Configuration
Manager.
Your company purchases Office 2013.
You need to ensure that all users can install Office 2013 from the Application Catalog.
What should you do?
A. Deploy Office 2013 by using a Group Policy Object (GPO).
B. Deploy a new application for Office 2013.
C. Update the Office 2010 source fileand redeploy the package.
D. Deploy a new package for Office 2013.
Answer: B

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. 相互情報
B. ケンドール相関
C. 順列機能の重要性
D. ムードの中央値検定
Answer: B
Explanation:
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter t), 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