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NEW QUESTION: 1
A. Option B
B. Option D
C. Option C
D. Option A
Answer: D
Explanation:
Configuring the debugger to break for first chance exceptions
To change when the debugger breaks, go to Debug->Exceptions...
When you first open this window you will see that there is a tree grid with one column and checkboxes.
* Break when Thrown. This includes a default list of exceptions known by the debugger, grouped by category.
Note: The possible exceptions that could break from this list is determined by the runtime you are debugging. For example, if you are using managed-only debugging then the debugger will never break for C++, Win32 Exceptions, etc. even if they are configured to break when thrown.
* Checkboxes. If you check the box for a category, then the debugger will break for all First
Chance Exceptions while debugging. If you don't want to enable all First Chance
Exceptions, you can find the specific exception types that you wish to configure by using the search box.
References:
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:
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 command lists all files in the current directory that start with a capital letter?
A. ls [A-Z]*
B. list-uppercase-files
C. ls A-Z
D. ls A-Z*
E. ls --uppercasefiles
Answer: A
NEW QUESTION: 4
You view several records using the Microsoft Dynamics CRM for tablets app.
After you view the records, you begin to work offline.
Which functionality is available for the offline records?
A. You can view and edit the records.
B. The same functionality available in the browser client is available on the tablet.
C. You can only view the records.
D. No access to recently viewed records is available, you can only create new, draft records.
Answer: A