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NEW QUESTION: 1
A WebLogic entity has been selected for analysis using Oracle IT Analytics.
Identify one method to confirm that data for the WebLogic entity is being successfully uploaded to the Oracle Management Cloud.
A. Check the agent collector logs to verify that the data for the WebLogic entity is getting harvested.
B. Use the Oracle IT Analytics Administration page to find out which WebLogic entities are receiving data.
C. Check the gateway logs to verify that the data for the WebLogic entity is being sent to OMC.
D. Check for the entity in the App Server Resource Analytics or App Server Performance Analytics dashboards.
Answer: D

NEW QUESTION: 2
A travel company wants to segment users based on their activity across a maximum of four devices. Which Profile Merge Rule configuration should be used?
A. Current Authentication Profile + a Data Source + No Device
B. Last Authentication Profile + a Data Source + Current Device Profile
C. Current Authentication Profile + a Data Source + Profile Link Device Profile
D. No Authentication Profile + a Data Source + Current Device Profile
Answer: C

NEW QUESTION: 3
If you wish to allow recording on a call, and recording is configured for two devices on the call. Which one of the following scenarios is possible?
A. Call recording will be started when both of the devices signal EventEstablished.
B. Call will not be recorded
C. Call recording will only be started when either one of the devices first signals Eventreleased.
D. Call recording will be started only on the device that first signals EventEstablished.
Answer: D
Explanation:
Reference: http://genesyslab.info/wiki/index.php/Special:Repository/80fr_depsip.pdf?id=6483fb5b-9ea1-43a0-8aeb-f38b19fb29a7 (page 98)

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. ムードの中央値検定
D. 順列機能の重要性
Answer: B
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