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NEW QUESTION: 1
Which two statements are true concerning Administrator Groups in FileMaker Server 12? (Choose two.)
A. Administrator Groups may be nested.
B. A group administrator can limit the numbers of clients that can access hosted files within their Administrator Group.
C. A group administrator can change the FileMaker security settings for all databases in their Administrator Group
D. The Administrator Groups feature is only available in FileMaker Server 12 Advanced.
E. A group administrator can disconnect clients from databases in their Administrator Group.
Answer: C,D

NEW QUESTION: 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have two servers that run Windows Server 2016.
You plan to create a Network Load Balancing (NLB) cluster that will contain both servers.
You need to configure the network cards on the servers for the planned NLB configuration.
Solution: You configure the network cards to be on the same subnet and to have static IP addresses. You configure the cluster to use multicast.
Does this meet the goal?
A. No
B. Yes
Answer: B
Explanation:
References:
https://technet.microsoft.com/en-us/windows-server-docs/networking/technologies/network- load-balancing

NEW QUESTION: 3
GPUベースのトレーニングを使用して、Azure Machine Learningサービスで画像認識のためのディープラーニングモデルを作成します。
リアルタイムのGPUベースの推論を可能にするコンテキストにモデルをデプロイする必要があります。
モデル推論のためにコンピューティングリソースを構成する必要があります。
どの計算タイプを使用する必要がありますか？
A. Azureコンテナーインスタンス
B. Azure Kubernetesサービス
C. 機械学習コンピューティング
D. フィールドプログラマブルゲートアレイ
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