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NEW QUESTION: 1

A. Option D
B. Option B
C. Option E
D. Option C
E. Option A
F. Option F
Answer: B,C,D
Explanation:
Explanation
References:
https://technet.microsoft.com/en-us/library/dn440164(v=exchg.160).aspx?f=255&MSPPError=-2147217396
https://technet.microsoft.com/en-us/library/dd353189(v=exchg.160).aspx

NEW QUESTION: 2
Which option describesthe relationship between IT and OT budgets?
A. Bothareshrinking al the roughly thesame rate.
B. ITand OTbudgets aregrowingat about the same rate.
C. OTbudgetsaregrowingfasterthan ITbudgets.
D. IT budgets are growing fasterthan OTbudgets.
Answer: C

NEW QUESTION: 3
A solution designer creates a custom Case page in the Case Client. However, when adding this page, the analyst does not duplicate the default Case page in the solution space. The analyst enhances the page by adding the out of the box Case List widget, a Case Search widget, and a website widget from the catalog. Which action must the analyst perform to make the custom page functional?
A. Add the Command widget and manually wire this widget to the Case List and Case Search widgets.
B. Add a script adapter widget and wire it to the Case List and Case Search widgets.
C. Add the Command widget which automatically wires to the Case List and Case Search widgets.
D. Add the Case Information Widget from the catalog.
Answer: C

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