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NEW QUESTION: 1
Which statement describes the purpose of the Payload Type field in the RTP header?
A. It identifies the port numbers for RTP.
B. It identifies the codec.
C. It identifies the port numbers for RTCP.
D. It identifies the signaling protocol.
Answer: B
Explanation:
PT, Payload Type. 7 bits: Identifies the format of the RTP payload and determines its interpretation by the application.
A profile specifies a default static mapping of payload type codes to payload formats. Additional payload type codes
may be defined dynamically through non-RTP means. An RTP sender emits a single RTP payload type at any given
ti me; this field is not intended for multiplexing separate media streams. A full list of codecs and their payload type
values can be found at the link below:
Reference: http://www.networksorcery.com/enp/protocol/rtp.htm

NEW QUESTION: 2
What is the purpose of the Starter system in the SAP S/4HANA Cloud implementation project?
Response:
A. To configure the customer's personalized solution for demonstration and testing during the Realize
phase
B. To conduct a blueprint workshop to identify all customer requirements for a completely customized
solution
C. To demonstrate the customer's personalized solution, including process flows, the organizational
structure, and authorizations
D. To demonstrate SAP Best Practices processes in a preconfigured system and identify customer
personalization requirements
Answer: D

NEW QUESTION: 3
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. ケンドール相関
B. ムードの中央値検定
C. 順列機能の重要性
D. 相互情報
Answer: A
Explanation:
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter τ), 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