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NEW QUESTION: 1
What happens when you enable the assignment parameter "Limit work by points"?
A. If a resource has a point value configured on their Resource Information screen, routing will limit assignment of additional activities if the cumulative point total exceeds the resource's point limit
B. Routing will use each activity's unique point value to prioritize assignment of higher point activities over lower point activities.
C. Routing will not assign a given activity if that activity's point value exceeds the resource's maximum personal point limit.
D. If resources have shifts assigned to them with point values, routing will not assign activities whose cumulative point total exceeds the shift point value.
Answer: A
Explanation:
Explanation
https://docs.oracle.com/cd/E62445_01/4.5_SmartRouting_User_Guide.pdf

NEW QUESTION: 2
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.

NEW QUESTION: 3
Dawn has been given a PNG file to use in the application icon. How should she do this?
A. Reproduce the image manually using the icon editor.
B. Enter the file name into the icon section of the application properties.
C. Import the file as an image resource, and then insert into the icon properties.
D. Replace the application icon with the file, as PNG files are supported.
Answer: D