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NEW QUESTION: 1
Linux管理者は、ディスク使用量が多いユーザーを識別する必要があります。管理者は＃du -s / home / *コマンドを実行し、次の出力を取得します。
出力に基づいて、User3が使用するディスク容量が最大になります。ファイル領域をクリーンアップするには、管理者は、ディスク領域を最も使用している特定のファイルに関する詳細情報を見つける必要があります。
次のコマンドのどれがこのタスクを完了しますか？
A. df -k /home/User/files.txt
B. du -sh / home / User /
C. を見つけます。 -name / home / User3 -print
D. du -a / home / User3 / *
Answer: B
NEW QUESTION: 2
A Network Administrator wants to block all social media typo websites, including Facebook and Google+.
Which Network Object type should be used to achieve a broad match on all social media websites?
A. URL Category Object
B. lnspection Object
C. URL List Object
D. Address Object
Answer: B
NEW QUESTION: 3
モデルをトレーニングして、Azure Machine Learningワークスペースに登録します。これで、モデルをリアルタイムWebサービスとしてデプロイする準備が整いました。
モデルをAzure Kubernetes Service（AKS）推論クラスターにデプロイしますが、サービスがモデルのデプロイメントに関連付けられているエントリスクリプトを実行するとエラーが発生するため、デプロイメントは失敗します。
コードを更新するたびにサービスを再デプロイすることなく、コードを繰り返し変更してサービスをリロードすることにより、エラーをデバッグする必要があります。
あなたは何をするべきか？
A. Azureコンテナーインスタンス（ACI）Webサービスデプロイメント構成を作成し、ACIにモデルをデプロイします。
B. モデルの新しいバージョンを登録し、エントリースクリプトを更新して、登録されたパスからモデルの新しいバージョンをロードします。
C. ローカルWebサービスデプロイメント構成を作成し、モデルをローカルDockerコンテナーにデプロイします。
D. エントリスクリプトの最初の行にブレークポイントを追加し、サービスをAKSに再デプロイします。
E. AKSサービスの展開構成を変更して、アプリケーションの洞察を有効にし、AKSに再展開します。
Answer: A
Explanation:
Explanation
How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and Azure Kubernetes Service (AKS) using Azure Machine Learning.
The recommended and the most up to date approach for model deployment is via the Model.deploy() API using an Environment object as an input parameter. In this case our service will create a base docker image for you during deployment stage and mount the required models all in one call. The basic deployment tasks are:
1. Register the model in the workspace model registry.
2. Define Inference Configuration:
a. Create an Environment object based on the dependencies you specify in the environment yaml file or use one of our procured environments.
b. Create an inference configuration (InferenceConfig object) based on the environment and the scoring script.
3. Deploy the model to Azure Container Instance (ACI) service or to Azure Kubernetes Service (AKS).