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NEW QUESTION: 1
Windows Server 2016を実行するServer1という名前のサーバーがあります.Server1は、VM1という名前の仮想マシンをホストするHyper-Vホストです。
Server1には、vSwitch1、vSwitch2、およびvSwitch3という名前の仮想スイッチに接続された3つのネットワークアダプタカードがあります。
展示に示すように、VM1でNICチーミングを設定します。 （展示ボタンをクリックしてください）。

物理ネットワークアダプタカードがServer1で故障した場合、VM1がネットワークへのアクセスを保持することを保証する必要があります。
あなたは何をするべきか？
A. From Windows PowerShell on Server1, run the Set-
VmNetworkAdapterFailoverConfiguration cmdlet.
B. From Windows PowerShell on Server1, run Set-VmNetworkAdapter cmdlet.
C. From the properties of the NIC team on VM1, add the adapter named Ethernet to the NIC team.
D. From Windows PowerShell on VM1, run the Set-VmNetworkAdapterTeamMapping cmdlet.
Answer: B
Explanation:
Explanation
References: https://www.techsupportpk.com/2017/01/nic-teaming-in-hyper-v-on-windows-server-2016.html To prepare the guest VM1 to run NIC Teaming at guest level, you have to reconfigure VM1's network adapter setting:-Get-VM VM1 | Set-VMNetworkAdapter -AllowTeaming On
https://technet.microsoft.com/en-us/itpro/powershell/windows/hyper-v/set-vmnetworkadapter
https://docs.microsoft.com/en-us/windows-server/networking/technologies/nic
-teaming/create-a-new-nic-team-in-a-vm

NEW QUESTION: 2
You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.
You must deploy the model to a context that allows for real-time GPU-based inferencing.
You need to configure compute resources for model inferencing.
Which compute type should you use?
A. Machine Learning Compute
B. Azure Kubernetes Service
C. Field Programmable Gate Array
D. Azure Container Instance
Answer: B
Explanation:
You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for inference.
Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus

NEW QUESTION: 3
---
You are analyzing a Windows client application that uses Microsoft Visual Studio 2010 and Microsoft SQL Server 2008.
The application updates two database tables from the main user interface (UI) thread.
You need to ensure that the following requirements are met:
The database tables are either updated simultaneously or not updated at all.
Users are notified of the success or failure of the updates.
Users are able to perform other tasks during the update process.
What should you do?
A. Move the database update logic to a BackgroundWorker thread.
Ensure that the thread is enclosed in a TransactionScope Using block in the BackgroundWorkerDoWork method.
B. Use TransactionScope in a Using block on the UI thread.
Batch the database updates by setting the DbDataAdapter.UpdateBatchSize property to 2.
C. Use TransactionScope in a Using block on the UI thread.
Create a DependentTransaction object within the block and pass the object to the BackgroundWorkerReportProgress method.
Use the object in the ReportProgress method to create a new TransactionScope block.
D. Use TransactionScope in a Using block on the main thread.
Create a BackgroundWorker thread within the block.
Move the database updates to the BackgroundWorkerDoWork method.
Answer: A