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NEW QUESTION: 1
On AIX when migrating to MQv9, which command is required to make the latest version of the installation the primary installation?
A. setmqaut
B. setmqinst
C. setmqm
D. setmwenv
Answer: B

NEW QUESTION: 2
HOTSPOT
You have the following code (line numbers are included for reference only): To answer, complete each statement according to the information presented in the code.


Answer:
Explanation:

NEW QUESTION: 3
You are designing a SQL Server 2008 Integration Services (SSIS) package. The package will be used to extract data from a SQL Server 2008 database and load data to a Microsoft Office Excel workbook.
You need to ensure that the package creates a new worksheet in an existing Excel workbook during execution.
Which control flow component should you use to create the worksheet?
A. Execute SQL Task
B. Bulk Insert Task
C. Data Flow Task along with the OLE DB source
D. File System Task
Answer: C
Explanation:
Data flow tasks within SSIS can be used to move data from a source to destination and at the same time use certain transformation tasks to clean and modify the data while it is transferred. By adding a data flow task to the package control flow the package can be made to perform data extraction, transformation and load (ETL) operations.
A: File System Task is used to perform operations on files and folders, such as create, move, delete, and also to set the attributes for files and folders.
B: The Execute SQL Task can be used to execute any TSQL code
D: The Bulk Insert Task can be used to insert data from a text file or a flat file into a SQL Server Table

NEW QUESTION: 4
トレーニングと検証のエラー値に大きな差があるモデルがあります。
新しいモデルを作成し、相互検証を実行する必要があります。
Azure Machine Learning Studioを使用して、新しいモデルのパラメーターセットを識別する必要があります。
各ステップで使用するモジュールはどれですか？答えるには、適切なモジュールを正しい手順にドラッグします。
各モジュールは、1回または複数回使用することも、まったく使用しないこともできます。コンテンツを表示するには、ペイン間で分割バーをドラッグするか、スクロールする必要がある場合があります。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample