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NEW QUESTION: 1
Which two of the following tasks would require a restart of the Alfresco server to apply the changes?
A. Deploying a new content model into Data Dictionary/Models.
B. Making configuration changes via JMX.
C. Deploying a new workflow through the Activities workflow console.
D. Deploying a new content model via a *-context.xml file.
E. Making configuration changes in alfresco-global properties.
Answer: B,E
Explanation:
Explanation
References
http://docs.alfresco.com/4.0/tasks/global-props-config.html
http://docs.alfresco.com/6.0/tasks/OOo-subsystems-config.html
NEW QUESTION: 2
새 Azure AD (Azure Active Directory) 역할을 만들려고 합니다.
새 역할이 Azure 구독의 모든 리소스를 보고 Microsoft에 지원 요청을 보낼 수 있는지 확인해야 합니다. 솔루션은 최소 권한의 원칙을 사용해야 합니다.
JSON 정의를 어떻게 완료해야 합니까? 답변하려면 답변 영역에서 적절한 옵션을 선택하십시오.
참고 : 각각의 올바른 선택은 1 포인트의 가치가 있습니다.
Answer:
Explanation:
설명
상자 1 : "* / 읽기",
* / read는 모든 것을 볼 수 있지만 변경하지는 않습니다.
상자 2 : "Microsoft.Support/*"
Microsoft.Support/* 작업을 통해 지원 티켓을 만들고 관리할수 있습니다.
참고 문헌 :
https://docs.microsoft.com/en-us/azure/role-based-access-control/tutorial-custom-role-powershell
https://docs.microsoft.com/en-us/azure/role-based-access-control/built-in-roles
NEW QUESTION: 3
Azure Machine Learning Studioで線形回帰モデルを開発しています。さまざまなアルゴリズムを比較する実験を実行します。
次の画像は、結果データセットの出力を示しています。
ドロップダウンメニューを使用して、画像に表示される情報に基づいて各質問に回答する回答の選択肢を選択します。
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:
説明
ボックス1：決定木回帰のブースト
平均絶対誤差（MAE）は、予測が実際の結果にどれだけ近いかを測定します。したがって、スコアが低いほど優れています。
ボックス2：
オンライン勾配降下法：アルゴリズムで最適なパラメーターを見つけるには、[トレーナーモードの作成]オプションを[パラメーター範囲]に設定します。その後、試行するアルゴリズムに複数の値を指定できます。
参照：
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION: 4
What topic attribute/value pair controls delivery of all non-persistent publications to all subscribers?
A. PUBDLV(NPSISTALL)
B. MSGDLV(NPSISTALL)
C. NPMSGDLV(ALL)
D. NPPUBDLV(ALL)
Answer: C