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NEW QUESTION: 1
Refer to the Exhibit for the setup of queue managers in a company. An application connected to QMGR1 needs to send messages to queue QMGR3.LOCAL. The administrator creates the following definition on QMGR1: DEFINE QREMOTE(QMGR3.REMOTE) +RQMNAME(QMGR3)+
RNAME(QMGR3.LOCAL) + XMITQ(QMGR2) The application puts 7000 messages upon starting onQMGR3.REMOTE which results on a queue full condition on QMGR3.LOCAL. Where would the excess messages end up?
A. QMGR3.DEAD
B. Transmit Queue QMGR2
C. Transmit Queue QMGR3
D. QMGR2.DEAD
Answer: A

NEW QUESTION: 2
/ etc / passwdのパスワードフィールドのどの文字が、暗号化されたパスワードが/ etc / shadowに保存されていることを示すために使用されていますか？
A. x
B. -
C. s
D. *
Answer: A

NEW QUESTION: 3
Scenario: A Citrix Architect is designing a XenApp and XenDesktop environment and needs to design machine catalogs and Delivery Groups. Users should only be able to access their assigned resources.
Click on the Exhibit button to view the design requirements.

What is the appropriate design using the minimum number of machine catalogs and Delivery Groups, based on the requirements in the Exhibit?
A. 1 machine catalog, 1 Delivery Group, and tagging for the machine hosting the Engineering application - QA version
B. 3 machine catalogs, 4 Delivery Groups, and no tagging
C. 1 machine catalog, 2 Delivery Groups, and no tagging
D. 2 machine catalogs, 3 Delivery Groups, and tagging for the machine hosting the Engineering application - QA version
Answer: C

NEW QUESTION: 4
You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the transformations.
Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: Clean Missing Data
Box 2: SMOTE
Use the SMOTE module in Azure Machine Learning Studio to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
Box 3: Convert to Indicator Values
Use the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.
Box 4: Remove Duplicate Rows
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values