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NEW QUESTION: 1
Which three options relate to event-driven RRM? (Choose three.)
A. any 802.11n AP models
B. configurable in WLC at 802.11b/g/n > RRM > TPC
C. minimum of AP, WLC, and WCS
D. specific AP models
E. minimum of AP, WLC, WCS, and MSE
F. configurable in WLC at 802.11b/g/n > RRM > DCA
G. minimum of AP and WLC
Answer: D,F,G
Explanation:
Explanation/Reference:
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:
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
Which factors create an impetus for customers to invest in communications? (Choose two)
A. The rise in new Internet-style voice services from reliable public network providers.
B. The demand of Internet users for tools and software that help them in online transactions.
C. The ease of providing users with software that helps them use their PCs as a part of the communication system.
D. The customers' desire to take advantage of cheaper digital phone services.
Answer: A,C

NEW QUESTION: 4
Which is employed by Cisco Nexus 7000 switching products to create multiple logical switches?
A. VTP
B. VLAN
C. VRF
D. vPC
E. VDC
Answer: E