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NEW QUESTION: 1
An LTM Specialist configured a virtual server to load balance a custom application. The application works when it is tested from within the firewall but it fails when tested externally. The pool member address is 192.168.200.10:80. A capture from an external client shows:
GET /index.jsp HTTP/1.1 Host: 207.206.201.100 User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:15.0) Gecko/20100101
Firefox/15.0.1 Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 Connection: keep-alive HTTP/1.1 302 Found DatE.Wed, 17 Oct 2012 23:09:55 GMT Server: Apache/2.2.15 (CentOS) Location: http://192.168.200.10/user/home.jsp Content-LengtH.304 Connection: close
What is the solution to this issue?
A. Add a Web Acceleration Profile to the virtual server.
B. Configure a content filter on the backend web server.
C. Assign a SNAT pool to the virtual server.
D. Configure redirect rewrite option in the HTTP profile.
Answer: D

NEW QUESTION: 2
The continue statement in the Python language is used to jump out of this loop and then continue to execute the next round of statements.
A. False
B. True
Answer: B

NEW QUESTION: 3
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: 4
In order to prevent other users from editng files in 'C:\ Files', which of the following steps should be taken?
A. Set the folder to index files for search
B. Set network share permissions to read only
C. Set NTFS permissions to read only
D. Set the local administrator as the owner of the folder
Answer: C