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NEW QUESTION: 1
A large organization is considering a policy that would allow employees to briog their own smartphones into the organizational environment. The MOST important concern to the information security manager should be the:
A. higher costs in supporting end users.
B. impact on network capacity.
C. decrease in end user productivity.
D. lack of a device management solution.
Answer: D

NEW QUESTION: 2
セキュリティアナリストがBIAドキュメントを更新しています。 セキュリティアナリストは、サーバーのハードドライブを交換するサポートベンダーの時間が8時間から2時間になったことに気付きました。 これらの新しい指標を考慮して、次のうちどれを結論付けることができますか？ （2つ選択）
A. MTTFが減少しました。
B. MTTRは遅いです。
C. MTTRは高速です。
D. RTOが増加しました。
E. MTTFが増加しました。
F. RTOは減少しました。
Answer: C,F

NEW QUESTION: 3
Azure Machine Learning Designerを使用して、機械学習モデルを作成します。モデルをAzureKubernetes Service（AKS）推論コンピューティングクラスターでリアルタイムサービスとして公開します。デプロイされたエンドポイント構成に変更を加えることはありません。
アプリケーション開発者に、エンドポイントを使用するために必要な情報を提供する必要があります。
アプリケーション開発者に提供する必要がある2つの値はどれですか？それぞれの正解は、解決策の一部を示しています。
注：正しい選択はそれぞれ1ポイントの価値があります。
A. エンドポイントの推論パイプライン実験の実行ID。
B. エンドポイントの推論パイプラインの名前。
C. エンドポイントのURL。
D. エンドポイントのキー。
E. エンドポイントがホストされているAKSクラスターの名前。
Answer: C,D
Explanation:
Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model.
You create a web service when you deploy a model to your local environment, Azure Container Instances, Azure Kubernetes Service, or field-programmable gate arrays (FPGA). You retrieve the URI used to access the web service by using the Azure Machine Learning SDK. If authentication is enabled, you can also use the SDK to get the authentication keys or tokens.
Example:
# URL for the web service
scoring_uri = '<your web service URI>'
# If the service is authenticated, set the key or token
key = '<your key or token>'
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service

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