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NEW QUESTION: 1
You are preparing to deploy a Windows Store app to the marketplace.
You need to ensure that unhandled errors and exceptions are never displayed to users.
What should you do?
A. In the app object, wrap the call to the Window.Current.Activate() method in a try-catch statement to handle any exceptions that occur.
B. Write a handler for the UnhandledException event and set the Handled property on the UnhandledExceptionEventArgs object to true.
C. Write a handler for the UnhandledException event and set the CancelBubble property on the UnhandledExceptionEventArgs object to true.
D. Open the Exceptions window in Visual Studio and clear the check box for unhandled exceptions for the Common Language Runtime.
Answer: B
NEW QUESTION: 2
Asset classes consist of (Choose Two)
A. a depreciation area section
B. None of the above
C. a master data section
D. All of the above
Answer: A,C
NEW QUESTION: 3
You are building an experiment using the Azure Machine Learning designer.
You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.
You need to determine the Area Under the Curve (AUC) of the model.
Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:
Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.
Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.