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NEW QUESTION: 1
展示を参照してください。
このデバイスのNAT設定で、内部インターフェイスと外部インターフェイスを正しく識別しました。
この構成のどの効果が本当ですか？
A. NAT64
B. ダイナミックNAT
C. スタティックNAT
D. PAT
Answer: D
NEW QUESTION: 2
システム管理者がAWSでアプリケーションを管理しています。アプリケーションはEC2にインストールされ、ユーザーはELBとAuto Scalingを設定しました。将来の負荷の増加を考慮して、ユーザーは、ELBに登録されるように、新しいサーバーを積極的に起動することを計画しています。
ユーザーはこれらのインスタンスをAuto Scalingでどのように追加できますか？
A. Auto Scalingグループの必要な容量を増やします
B. インスタンスを手動で起動し、その場でELBに登録します
C. Auto Scalingグループの上限を増やす
D. Auto Scalingグループの最小制限を引き下げます
Answer: A
Explanation:
Explanation
A user can increase the desired capacity of the Auto Scaling group and Auto Scaling will launch a new instance as per the new capacity. The newly launched instances will be registered with ELB if Auto Scaling group is configured with ELB. If the user decreases the minimum size the instances will be removed from Auto Scaling. Increasing the maximum size will not add instances but only set the maximum instance cap.
http://docs.aws.amazon.com/AutoScaling/latest/DeveloperGuide/as-manual-scaling.html
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. フィールドプログラマブルゲートアレイ
B. Azureコンテナーインスタンス
C. 機械学習コンピューティング
D. Azure Kubernetesサービス
Answer: D
Explanation:
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