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NEW QUESTION: 1
Which statement is FALSE about Autonomous Database Maintenance on dedicated Exadata infrastructure?
A. If a scheduled container database maintenance run cannot take place, Oracle will automatically reschedule the container database maintenance for the following quarter.
B. Autonomous Exadata Infrastructure maintenance runs are for infrastructure patching (including patching of the Exadata grid infrastructure code and operating systems updates), and include database patching.
C. Autonomous Exadata Infrastructure maintenance takes place at least once each quarter and is mandatory.
D. You can change your container database maintenance window or reschedule a single container database maintenance run to ensure that your container database maintenance runs follow infrastructure maintenance within the same quarter.
Answer: B

NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Azureサブスクリプションがあります。
Serverという名前のオンプレミスファイルサーバーがあります。 Windows Server 2019を実行します。
あなたはサーバーを管理します！ Windows管理センターを使用する。
もしサーバーならそれを確実にする必要があります！失敗すると、サーバーを回復できます！ Azureからのファイル。
解決策：AzureにWindows管理センターを登録し、Azureバックアップを構成します。
これは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
Explanation
Instead use Azure Storage Sync service and configure Azure File.
Use Azure File Sync to centralize your organization's file shares in Azure Files, while keeping the flexibility, performance, and compatibility of an on-premises file server. Azure File Sync transforms Windows Server into a quick cache of your Azure file share.
Reference:
https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction

NEW QUESTION: 3
C-Support Vector分類を使用して、不均衡なトレーニングデータセットでマルチクラス分類を行っています。以下に示すPythonコードを使用したC-Support Vector分類：

C-Support Vector分類コードを評価する必要があります。
どの評価ステートメントを使用する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0)
Penalty parameter C of the error term.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html