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NEW QUESTION: 1
Your company has an Office 365 subscription that is configured for single sign-on (SSO) to an on-premises deployment of Active Directory.
Office 2016 is deployed to all workstations. Microsoft OneDrive for Business is used to replicate My Documents to OneDrive for Business.
You need to ensure that when clients connect to Office 365 from an untrusted network, they can access Office
365 resources by using a web browser.
Which two actions should you perform? Each correct answer presents part of the solution.
A. Add a relying party trust.
B. Add a claims provider trust.
C. Disable modern authentication.
D. Add a new rule.
E. Modify the Sharing settings for SharePoint Online.
Answer: B,C
Explanation:
Explanation
B: In Skype for Business Server 2015, Modern Authentication is used between on-premises clients and on-premises servers in order to give users a proper level of authorization to resources.
C: A Claims Provider trust is one where ADFS gets claims from the Claim Provider, which could be the local AD as Claims Provider or an external Claims Provider.

NEW QUESTION: 2
When creating a VM on an AHV cluster, how is the initial placement of the VM determined?
A. The Acropolis Dynamic Scheduler selects a host which provides adequate resources for the VMs configuration
B. The administrator right clicks on the desired host and selects Power on VM from the dropdown menu
C. Placement is determined by the host that holds the new_VM token at the time of VM creation
D. AHV uses a round robin algorithm, placing new VMs onto hosts based on the numerical order of their UUID
Answer: A

NEW QUESTION: 3
英語のテキストコンテンツをフランス語のテキストコンテンツに翻訳するための機械学習モデルを構築しています。
テキストコンテンツのシーケンスを学習するには、機械学習モデルを構築してトレーニングする必要があります。
どのタイプのニューラルネットワークを使用する必要がありますか？
A. Multilayer Perceptions (MLPs)
B. Convolutional Neural Networks (CNNs)
C. Recurrent Neural Networks (RNNs)
D. Generative Adversarial Networks (GANs)
Answer: C
Explanation:
Explanation
To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).
Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both.
They're called recurrent because the network's hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
References:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571