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NEW QUESTION: 1
Drag and drop the IPv6 IP addresses from the left onto the correct IPv6 address types on the right
Answer:
Explanation:
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. Yes
B. No
Answer: A
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
Click the Exhibit button.
You have configured an IDP policy as shown in the exhibit. The configuration commits successfully. Which traffic will be examined for attacks?
A. recommended traffic between the source and destination hosts
B. only reply traffic from destination to source in a session
C. both originating and reply traffic between hosts in a session
D. only originating traffic from source to destination in a session
Answer: C
Explanation:
Explanation/Reference:
Reference: http://www.juniper.net/techpubs/software/junos-security/junos-security96/junos-security- swconfig-security/config-idp-ips-rulebase-section.html#config-idp-ips-rulebase-section
NEW QUESTION: 4
The company's security officer has stated that for compliance reasons, all internal and external network traffic must be encrypted.
How should L2TP be implemented?
A. By using NetScaler
B. By implementing secure delivery groups
C. By installing a Windows Server operating system with Routing and Remote Access Service
D. By implementing an Active Directory group policy to configure L2TP
Answer: D