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NEW QUESTION: 1
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 Kubernetesサービス
C. Azureコンテナーインスタンス
D. 機械学習コンピューティング
Answer: B
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
NEW QUESTION: 2
ユーザーがVPCウィザードを使用して、パブリックサブネットとプライベートサブネットでVPCを作成しました。 VPCにはCIDRがあります
20.0.0.0/16。パブリックサブネットはCIDR 20.0.1.0/24を使用します。ユーザーは、パブリックサブネット(ポート80.およびプライベートサブネット(ポート3306)のWebサーバーをホストすることを計画しています。ユーザーは、パブリックサブネット(WebSecGrp)およびプライベートサブネット(DBSecGrp)のセキュリティグループを構成しています)。プライベートサブネットデータベースセキュリティグループ(DBSecGrp)に必要なエントリは次のうちどれですか?
A. Allow Inbound on port 3306 for Source Web Server Security Group (WebSecGrp)
B. Allow Outbound on port 80 for Destination NAT Instance IP
C. Allow Outbound on port 3306 for Destination Web Server Security Group (WebSecGrp)
D. Allow Inbound on port 3306 from source 20.0.0.0/16
Answer: A
Explanation:
説明
ユーザーはVPCでサブネットを作成し、そのサブネット内でインスタンスを起動できます。ユーザーがWebサーバーとDBサーバーをそれぞれホストするパブリックプライベートサブネットを作成した場合、ユーザーはプライベートサブネットのインスタンスがDBポートのパブリックサブネットからインバウンドトラフィックを受信できるように構成する必要があります。したがって、ポートを構成する
3306は、Webサーバーセキュリティグループ(WebSecGrp)としてのソースとのインバウンドです。ルートテーブルがプライベートサブネットからNATインスタンスにトラフィックを誘導するため、ユーザーは宛先0.0.0.0/0のポート80および443を構成する必要があります。
NEW QUESTION: 3
次のうちどれがデフォルトでポート53を使用しますか?
A. RDP
B. DNS
C. QoS
D. DHCP
Answer: B
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