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NEW QUESTION: 1
You are building a regression model for estimating the number of calls during an event.
You need to determine whether the feature values achieve the conditions to build a Poisson regression model.
Which two conditions must the feature set contain? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. The label data must be non-discrete.
B. The label data must be whole numbers.
C. The label data must be a negative value.
D. The label data can be positive or negative.
E. The label data must be a positive value.
Answer: B,E
Explanation:
Explanation/Reference:
Explanation:
Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:
The response variable has a Poisson distribution.

Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.

A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with

non-whole numbers.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression

NEW QUESTION: 2
A user has reported sluggish performance to the help desk. A technician determines the user has been installing applications for entertainment, and one of them might be slowing down the system.
Which of the following tools would help the technician track down the troublesome application(s)?
A. regedit
B. gpupdate
C. defrag
D. msconfig
Answer: B

NEW QUESTION: 3
学習者はトレーナーに、コースに関連するトピック領域に関する質問をします。トレーナーはトピック領域に関する知識がありません。次のうち、質問に回答する最良の方法はどれですか？ （2つ選択）。
A. 休憩中に質問を調査し、回答を提供します。
B. 質問をクラスにリダイレクトします。
C. トピックに関する知識不足を認めて、クラスに進んでください。
D. そのトピックがどのようにクラスの範囲外にあるかを議論します。
E. もっともらしいと思われる答えを提供します。
Answer: A,B