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NEW QUESTION: 1
Given this class:

Choose the statement that best describes the potential to write a client implemented using JAX-WS to interact with the JAX-RS (restful) web service defined by this class (Choose one):
A. It is possible, since a JAX-WS proxy Port type can be generated to use a simple raw XML protocol to interact with a service at a specific fixed URL.
B. It is possible, since the JAX-WS API can be used to create a client that uses a simple raw XML protocol to interact with a service at a given URL.
C. It is impossible, since JAX-RS does not support the same extension mechanism via JAX-WS Handlers that JAX-WS supports.
D. It is impossible, since JAX-WS requires that the underlying interaction be SOAP-based, and JAX-RS does not support SOAP.
Answer: B

NEW QUESTION: 2
Microsoft 365 Enterprise E5サブスクリプションがあります。
App1という名前のクラウドベースのアプリをMicrosoft Azure Active Directory（Azure AD）エンタープライズアプリケーションリストに追加します。
次回App1に接続するときに、すべてのユーザーアカウントに対して2段階認証が強制されるようにする必要があります。
ポリシーからどの3つの設定を構成する必要がありますか？回答するには、回答領域で適切な設定を選択します。

Answer:
Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/active-directory/conditional-access/best-practices
https://techcommunity.microsoft.com/t5/Enterprise-Mobility-Security/Conditional-Access-now-in-the-new-Azure-portal/ba-p/250060

NEW QUESTION: 3
What is the ping response to a transmitted echo that needed to be fragmented and fragmentation was not allowed?
A. D
B. ...
C. M
D. U
Answer: C

NEW QUESTION: 4
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps. Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

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