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NEW QUESTION: 1
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.

NEW QUESTION: 2
Multi-AZ deployment is supported for Microsoft SQL Server DB Instances.
A. False
B. True
Answer: B

NEW QUESTION: 3
コストに敏感なお客様は、パフォーマンスの低いEMCストレージシステムを使用しており、コピーサービスに多額の投資を行っています。IBMは、EMCストレージソリューションを補完し、応答時間を改善するソリューションを提供するよう求められています。
技術専門家が推奨するIBMストレージソリューションはどれですか？
A. IBMフラッシュシステムA9000
B. IBM Elastic Storage Server
C. IBMフラッシュシステム900
D. フラッシュドライブを備えたIBM SVC
Answer: A

NEW QUESTION: 4
DRAG DROP
You are planning to deploy a database to Windows Azure SQL Database.
You need to design a stored procedure to update rows. The stored procedure must meet the following requirements:
* If the update fails, an error must be raised to the application and the update must be discarded.
* The stored procedure must be designed to maximize concurrency.
What should you include in your design?
To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:
Box 1: Begin an explicit transaction.
Box 2: Perform an update in a try block.
Box 3: Read the @@ROWCOUNT system variable.
Box 4: Raise an error and roll back the transaction if the row count is less than 1.
Box 5: Commit the transaction in a finally block.
Note:
* Read Committed is SQL Server's default isolation level.
* @@ROWCOUNT
eturns the number of rows affected by the last statement.
* Using TRY...CATCH in a transaction
The following example shows how a TRY...CATCH block works inside a transaction. The statement inside the TRY block generates a constraint violation error.
BEGIN TRANSACTION;
BEGIN TRY
-- Generate a constraint violation error.
DELETE FROM Production.Product
WHERE ProductID = 980;
END TRY
BEGIN CATCH
SELECT
ERROR_NUMBER() AS ErrorNumber
,ERROR_SEVERITY() AS ErrorSeverity
,ERROR_STATE() AS ErrorState
,ERROR_PROCEDURE() AS ErrorProcedure
,ERROR_LINE() AS ErrorLine
,ERROR_MESSAGE() AS ErrorMessage;
IF @@TRANCOUNT > 0
ROLLBACK TRANSACTION;
END CATCH;
IF @@TRANCOUNT > 0
COMMIT TRANSACTION;
GO

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