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NEW QUESTION: 1
永続ボリュームのどの再利用ポリシーでリソースの手動再利用が許可されますか？
A. 削除
B. コピー
C. リサイクル
D. 保持
Answer: D
Explanation:
Explanation
https://docs.openshift.com/container-platform/3.6/architecture/additional_concepts/storage.html#:~:text=accordin ReclaimingThe reclaim policy of a PersistentVolume tells the cluster what to do with the volume after it is released. Volumes reclaim policy can either be Retain, Recycle, or Delete.
Retain reclaim policy allows manual reclamation of the resource for those volume plug-ins that support it. Delete reclaim policy deletes both the PersistentVolume object from OpenShift Container Platform and the associated storage asset in external infrastructure, such as AWS EBS, GCE PD, or Cinder volume.

NEW QUESTION: 2
You write code to retrieve an experiment that is run from your Azure Machine Learning workspace.
The run used the model interpretation support in Azure Machine Learning to generate and upload a model explanation.
Business managers in your organization want to see the importance of the features in the model.
You need to print out the model features and their relative importance in an output that looks similar to the following.

How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: from_run_id
from_run_id(workspace, experiment_name, run_id)
Create the client with factory method given a run ID.
Returns an instance of the ExplanationClient.
Parameters
Workspace Workspace An object that represents a workspace.
experiment_name str The name of an experiment.
run_id str A GUID that represents a run.
Box 2: list_model_explanations
list_model_explanations returns a dictionary of metadata for all model explanations available.
Returns
A dictionary of explanation metadata such as id, data type, explanation method, model type, and upload time, sorted by upload time Box 3: explanation Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-interpret/azureml.contrib.interpret.explanation.explanation_client.explanationclient?view=azure-ml-py

NEW QUESTION: 3
ケーススタディ3-Proseware、Inc
バックグラウンド
あなたはProseware、Incの開発者です。Prosewareの内部サービス、外部サービス、およびアプリケーションに一連のガバナンスポリシーを適用するアプリケーションを開発しています。アプリケーションはまた、一般的な機能のための共有ライブラリを提供します。
必要条件
政策サービス
ポリシーサービスという名前のステートフルASP.NET Core 2.1 Webアプリケーションを開発してAzure App Service Webアプリにデプロイします。アプリケーションは、Azure Event Gridからのイベントに反応し、それらのイベントに基づいてポリシーアクションを実行します。
アプリケーションでは、すべてのApplication InsightsテレメトリにEvent Grid Event IDフィールドを含める必要があります。
ポリシーサービスは、Application Insightsを使用して、実行中のポリシーアクションの数に応じて自動的にスケーリングする必要があります。
ポリシー
ログポリシー
すべてのAzure App Service Webアプリは、Azure Blobストレージにログを書き込む必要があります。すべてのログファイルは、logdropという名前のコンテナーに保存する必要があります。ログは15日間コンテナに保持する必要があります。
認証イベント
認証イベントは、ユーザーのサインインとサインアウトを監視するために使用されます。すべての認証イベントは、ポリシーサービスによって処理される必要があります。サインアウトはできるだけ早く処理する必要があります。
PolicyLib
すべてのASP.NET Core Webサービスとアプリケーションに共通の機能を含むPolicyLibという名前の共有ライブラリがあります。 PolicyLibライブラリは次の条件を満たす必要があります。
* Application Insightsテレメトリからユーザー以外のアクションを除外します。
* Webサービスがそれ自体をスケーリングできるようにするメソッドを提供する
*スケーリングアクションがアプリケーションの使用を妨げないことを確認する
その他の
異常検知サービス
異常のログ情報を分析する異常検出サービスがあります。 Azure Machine Learningモデルとして実装されます。モデルはWebサービスとしてデプロイされます。
異常が検出された場合、管理者にメールを送信するAzure関数がHTTP WebHookを使用して呼び出されます。
健康モニタリング
すべてのWebアプリケーションおよびサービスには、/ healthサービスエンドポイントでヘルスモニタリングがあります。
ポリシーの喪失
ポリシーサービスを展開するときに、展開中に適用されている途中のポリシーは適用されない場合があります。
パフォーマンスの問題
負荷が高い場合、異常検出サービスはスローダウンし、接続を拒否します。
通知の待ち時間
ユーザーは、異常が検出されてから数分後に異常検出メールが届く場合があると報告しています。
アプリファイルの関連部分を以下に示します。行番号は参照用にのみ含まれており、それらが属する特定のファイルを示す2文字の接頭辞が含まれています。


アプリファイルの関連部分を以下に示します。行番号は参照用にのみ含まれており、それらが属する特定のファイルを示す2文字の接頭辞が含まれています。

通知の待ち時間の問題を解決する必要があります。
どの2つのアクションを実行する必要がありますか？それぞれの正解は、ソリューションの一部を示しています。
注：それぞれの正しい選択は1ポイントの価値があります。
A. Azure Functionが消費プランを使用するように設定されていることを確認します。
B. Azure FunctionがApp Serviceプランを使用していることを確認します。
C. 常にオンに設定します。
D. Always Onをfalseに設定します。
Answer: B,C
Explanation:
Azure Functions can run on either a Consumption Plan or a dedicated App Service Plan. If you run in a dedicated mode, you need to turn on the Always On setting for your Function App to run properly. The Function runtime will go idle after a few minutes of inactivity, so only HTTP triggers will actually "wake up" your functions. This is similar to how WebJobs must have Always On enabled.
Scenario: Notification latency: Users report that anomaly detection emails can sometimes arrive several minutes after an anomaly is detected.
Anomaly detection service: You have an anomaly detection service that analyzes log information for anomalies. It is implemented as an Azure Machine Learning model. The model is deployed as a web service.
If an anomaly is detected, an Azure Function that emails administrators is called by using an HTTP WebHook.
References:
https://github.com/Azure/Azure-Functions/wiki/Enable-Always-On-when-running-on-dedicated- App-Service-Plan