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NEW QUESTION: 1
電力網のどこに該当するかを特定するために、ステークホルダーについて知っておくべき2つの要素はどれですか。 （2つ選んでください。）
A. 興味の程度
B. 会社での役割
C. 購買力
D. 予算の大きさ
E. 影響度
Answer: A,E

NEW QUESTION: 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation
The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.
Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.
Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words:
ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
AUC.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION: 3
You are designing a plan to deploy a new application to Azure. The solution must provide a single sign-on experience for users.
You need to recommend an authentication type.
Which authentication type should you recommend?
A. SAML credential tokens
B. Windows Authentication
C. MS-CHAP
D. Azure managed access keys
Answer: A
Explanation:
A Microsoft cloud service administrator who wants to provide their Azure Active Directory (AD) users with sign-on validation can use a SAML 2.0 compliant SP-Lite profile based Identity Provider as their preferred Security Token Service (STS) / identity provider. This is useful where the solution implementer already has a user directory and password store on-premises that can be accessed using SAML 2.0. This existing user directory can be used for sign-on to Office 365 and other Azure AD-secured resources.
Reference: Use a SAML 2.0 identity provider to implement single sign-on
https://msdn.microsoft.com/en-us/library/azure/dn641269.aspx?f=255&MSPPError=2147217396

NEW QUESTION: 4
What technology can you implement on your network to allow IPv4-dependent applications to work with
IPv6- capable application?
A. ISATAP
B. NAT64
C. OS-lite
D. NAT 6to4
E. NAT-PT
Answer: B