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NEW QUESTION: 1

A. Primary Field Name
B. Solution Publisher Prefix
C. Description
D. Display Name
Answer: B
Explanation:
Ref:https://msdn.microsoft.com/en-us/library/gg309396.aspx

NEW QUESTION: 2
In Data ONTAP the root user is exempt from these two quotas: _______. (Choose two.)
A. root quotas
B. group quotas
C. user quotas
D. tree quotas
E. file quotas
Answer: B,C

NEW QUESTION: 3
Universal Containers would like to customize the security and sharing features of Salesforce Account Teams.
They have decided to implement a Custom Account Team object. They would like the new enhancement to include all of the features of the existing account team, but also utilize Apex and Visualforce on the custom Account Team object.
Which two different approaches should the Architect consider when designing this enhancement?
Choose 2 answers
A. The need to synchronize the AccountTeamMember object with the Custom Account Team object data.
B. The need to maintain the Account_share object based upon the Custom Account Team object data.
C. The need to customize Account screens in Visualforce, as the Account_share object cannot be maintained programmatically.
D. The need to dynamically create Criteria-Based Sharing rules with Custom Account Team object data.
Answer: A,B

NEW QUESTION: 4
Azure Machine Learningデザイナーを使用して実験を構築しています。
データセットをトレーニングセットとテストセットに分割します。 Two-Class Boosted Decision Treeをアルゴリズムとして選択します。
モデルの曲線下面積（AUC）を決定する必要があります。
どの3つのモジュールを順番に使用する必要がありますか？回答するには、適切なモジュールをモジュールのリストから回答領域に移動し、正しい順序に並べます。

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.