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NEW QUESTION: 1
Refer to the exhibit. Which three statements about the output are true? (Choose three.)
A. An mrouter port can be learned by receiving a PIM hello packet from a multicast router.
B. This switch is not configured as a multicast router. It is configured only for IGMP snooping.
C. An mrouter port is learned when a multicast data stream is received on that port from a multicast router.
D. Gi2/0/1 is a trunk link that connects to a multicast router.
E. This switch is configured as a multicast router.
F. IGMP reports are received only on Gi2/0/1 and are never transmitted out Gi2/0/1 for VLANs 10 and 20.
Answer: A,D,E
NEW QUESTION: 2
A. the Disk Management snap-in
B. exethe clussvc. command
C. the Failover Cluster Manager snap-in
D. the Cluster.exe command
E. the Computer Management snap-in
F. the Server Manger app
G. the configure hyperv.exe command
H. the Hyper-V Manager snap-in
Answer: H
NEW QUESTION: 3
A NYSE floor member executing an order for a public customer asks the specialist in the stock to
guarantee a price while giving the customer an opportunity to obtain a better price. This procedure is
known as:
A. a special deal
B. floor protection
C. a stop order
D. stopping stock
Answer: D
Explanation:
stopping stock. This is the definition of the term "stopping stock".
NEW QUESTION: 4
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