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NEW QUESTION: 1
Which command would disable a Cluster Member permanently?
A. cphaprob_admin down
B. clusterXL_ adimn down
C. set clusterXL down-p
D. clusterXL_admin down -p
Answer: B

NEW QUESTION: 2
Drag & Drop-Frage
Sie haben ein Azure-Abonnement, das von vier Abteilungen in Ihrem Unternehmen verwendet wird.
Das Abonnement enthält 10 Ressourcengruppen. Jede Abteilung verwendet Ressourcen in mehreren Ressourcengruppen.
Sie müssen einen Bericht an die Finanzabteilung senden.
In dem Bericht müssen die Kosten für jede Abteilung aufgeführt sein.
Welche drei Aktionen sollten Sie nacheinander ausführen? Verschieben Sie zum Beantworten die entsprechenden Aktionen aus der Liste der Aktionen in den Antwortbereich und ordnen Sie sie in der richtigen Reihenfolge an.

Answer:
Explanation:

Erläuterung:
Box 1: Weisen Sie jeder Ressource ein Tag zu.
Sie weisen Ihren Azure-Ressourcen Tags zu, die Metadaten geben, um sie logisch in einer Taxonomie zu organisieren. Nachdem Sie Tags angewendet haben, können Sie alle Ressourcen in Ihrem Abonnement mit diesem Tagnamen und -wert abrufen. Jede Ressource oder Ressourcengruppe kann maximal 15 Tag-Name / Wert-Paare enthalten. Tags, die auf die Ressourcengruppe angewendet werden, werden nicht von den Ressourcen in dieser Ressourcengruppe geerbt.
Kasten 2: Filtern Sie die Ansicht vom Kostenanalyse-Blade aus nach Tags. Überprüfen Sie nach dem Ausführen Ihrer Dienste regelmäßig, wie viel sie Sie kosten. Sie können die aktuelle Ausgabe- und Brennrate im Azure-Portal anzeigen.
Besuchen Sie das Abonnement-Fenster im Azure-Portal und wählen Sie ein Abonnement aus. Die Kostenaufschlüsselung und die Brennrate sollten im Popup-Fenster angezeigt werden. Klicken Sie in der Liste links auf Kostenanalyse, um die Kostenaufschlüsselung nach Ressourcen anzuzeigen. Warten Sie 24 Stunden, nachdem Sie einen Dienst hinzugefügt haben, damit die Daten ausgefüllt werden.
Sie können nach verschiedenen Eigenschaften wie Tags, Ressourcengruppen und Zeiträumen filtern. Klicken Sie auf Anwenden, um die Filter zu bestätigen, und auf Herunterladen, wenn Sie die Ansicht in eine CSV-Datei (Comma-Separated Values) exportieren möchten.
Kasten 3: Laden Sie den Nutzungsbericht herunter
Verweise:
https://docs.microsoft.com/de-de/azure/azure-resource-manager/resource-group-using-tags
https://docs.microsoft.com/en-us/azure/billing/billing-getting-started

NEW QUESTION: 3
Welche der folgenden Technologien gewährleistet die vollständige Geräteverschlüsselung auf einem Windows Mobile-Gerät?
A. UEFI
B. Passcode
C. BitLocker
D. TPM
Answer: D

NEW QUESTION: 4
You need to scale out SSAS. What should you do?
A. Create an empty cube on ServerC and link to the objects in the cube on ServerB.
B. Synchronize the cube from ServerB to ServerC each day.
C. Back up the cube on ServerB and restore it on ServerC each day.
D. Process the cube on both ServerB and ServerC each day.
Answer: B
Explanation:
1.Dedicated Processing Architecture
In this, an instance of Analysis Services is reserved to process all new, incoming data. After processing is done, the result is copied to query servers. The advantage of this architecture is that the query servers can respond to the queries without being affected by the processing operation. A lock is required only when data is updated or added to the cube. Analysis Services Cube Synchronization: By using this built-in Analysis Services functionality, you can move the delta data directly to the query servers.
2. Query/Processing Flipping Architecture
The dedicated processing server architecture solves most scale-out cases. However, the time required to move the data from the processing server to the query servers may be restrictive if updates happen at intervals shorter than a few hours. Even with SAN snapshots or read-only LUN, it will still take some time to dismount the LUN, set it online on the query server, and finally mount the updated cube on the query servers. In the query/processing flipping architecture, each instance of Analysis Services performs its own processing. Because each server does it own processing, it is possible that some servers will have more recent data than others. This means that one user executing a query may get a later version of the data than another use executing the same query concurrently.