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NEW QUESTION: 1
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. 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。
B. 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。
C. 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。
D. 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。
E. 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。
Answer: D
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).
NEW QUESTION: 2
Match the HPE OneView interconnect module state to its definition. (Not all states will be used.)
Answer:
Explanation:
Explanation
NEW QUESTION: 3
SIMULATION
Which umask value will result in the default access permissions of 600 (rw-------) for files and 700 (rwx------) for directories? (Specify only the numerical umask value.)
Answer:
Explanation:
0077, 077