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NEW QUESTION: 1
Azure HDInsightクラスターを使用するソリューションを管理します。
クラスタのパフォーマンスとステータスを監視するソリューションを実装する必要があります。
どのテクノロジーを使用する必要がありますか？
A. Ambari Web UI
B. Azure HDInsight REST API
C. Azure Log Analytics
D. Ambari REST API
E. Azure HDInsight .NET SDK
Answer: A
Explanation:
Explanation
Ambari is the recommended tool for monitoring utilization across the whole cluster. The Ambari dashboard shows easily glanceable widgets that display metrics such as CPU, network, YARN memory, and HDFS disk usage. The specific metrics shown depend on cluster type. The "Hosts" tab shows metrics for individual nodes so you can ensure the load on your cluster is evenly distributed.
The Apache Ambari project is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Apache Hadoop clusters. Ambari provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs.
References:
https://azure.microsoft.com/en-us/blog/monitoring-on-hdinsight-part-1-an-overview/
https://ambari.apache.org/
NEW QUESTION: 2
You create a virtual machine scale set named Scale1. Scale1 is configured as shown in the following exhibit.
The subscription contains the Azure SQL databases shown in the following table.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1:
The Autoscale scale out rule increases the number of VMs by 2 if the CPU threshold is 80% or higher. The initial instance count is 4 and rises to 6 when the 2 extra instances of VMs are added.
Box 2:
The Autoscale scale in rule decreases the number of VMs by 4 if the CPU threshold is 30% or lower. The initial instance count is 4 and thus cannot be reduced to 0 as the minimum instances is set to 2. Instances are only added when the CPU threshold reaches 80%.
References:
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-overview
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-best-practices
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-common-scale-patterns
NEW QUESTION: 3
Ihr Netzwerk enthält eine Active Directory-Domäne mit dem Namen contoso.com.
Alle Benutzerkonten befinden sich in einer Organisationseinheit mit dem Namen OU1. Alle Benutzer in der Marketingabteilung sind Mitglieder einer Gruppe mit dem Namen Marketing. Alle Benutzer in der Personalabteilung sind Mitglieder einer Gruppe mit dem Namen HR.
Sie erstellen ein Gruppenrichtlinienobjekt mit dem Namen GPO1. Sie verknüpfen GPO1 mit OU1. Sie konfigurieren die Gruppenrichtlinieneinstellungen von GPO1 so, dass jedem Benutzer zwei Verknüpfungen mit den Namen Link1 und Link2 auf dem Desktop hinzugefügt werden.
Sie müssen sicherstellen, dass Link1 nur auf dem Desktop der Benutzer in Marketing und Link2 nur auf dem Desktop der Benutzer in HR angezeigt wird.
Was solltest du konfigurieren?
A. Sicherheitsfilterung
B. Gruppenrichtlinienvererbung
C. Targeting auf Elementebene
D. WMI-Filterung
Answer: C
Explanation:
Erläuterung:
Sie können das Targeting auf Elementebene verwenden, um den Bereich einzelner Einstellungselemente zu ändern, sodass diese nur für ausgewählte Benutzer oder Computer gelten. Innerhalb eines einzelnen Gruppenrichtlinienobjekts (Group Policy Object, GPO) können Sie mehrere Einstellungselemente einfügen, von denen jedes für ausgewählte Benutzer oder Computer angepasst wurde und von denen jedes darauf abzielt, Einstellungen nur auf die relevanten Benutzer oder Computer anzuwenden.
Referenz: http://technet.microsoft.com/en-us/library/cc733022.aspx
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