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NEW QUESTION: 1
What are valid types of calculation view?
There are 3 correct answers to this question.
Response:
A. Cube with star schema
B. Dimension
C. Dimension with star schema
D. Cube
Answer: A,B,D
NEW QUESTION: 2
Sie entwickeln ein Docker / Go mit Azure App Service-Webanwendung für Container. Sie planen, den Container in einem App Service unter Linux auszuführen. Sie identifizieren ein Docker-Container-Image, das verwendet werden soll.
Keine Ihrer aktuellen Ressourcengruppen befindet sich an einem Speicherort, der Linux unterstützt. Sie müssen die Anzahl der erforderlichen Ressourcengruppen minimieren.
Sie müssen die Anwendung erstellen und eine Erstbereitstellung durchführen.
Mit welchen drei Azure CLI-Befehlen sollten Sie die Lösung entwickeln? Verschieben Sie zum Beantworten die entsprechenden Befehle aus der Befehlsliste in den Antwortbereich und ordnen Sie sie in der richtigen Reihenfolge an.
Answer:
Explanation:
Explanation
You can host native Linux applications in the cloud by using Azure Web Apps. To create a Web App for
Containers, you must run Azure CLI commands that create a group, then a service plan, and finally the web
app itself.
Step 1: az group create
In the Cloud Shell, create a resource group with the az group create command.
Step 2: az appservice plan create
In the Cloud Shell, create an App Service plan in the resource group with the az appservice plan create
command.
Step 3: az webapp create
In the Cloud Shell, create a web app in the myAppServicePlan App Service plan with the az webapp create
command. Don't forget to replace with a unique app name, and <docker-ID> with your Docker ID.
References:
https://docs.microsoft.com/mt-mt/azure/app-service/containers/quickstart-docker-go?view=sql-server-ver15
NEW QUESTION: 3
You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the transformations.
Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Clean Missing Data
Box 2: SMOTE
Use the SMOTE module in Azure Machine Learning Studio to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
Box 3: Convert to Indicator Values
Use the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.
Box 4: Remove Duplicate Rows
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values
NEW QUESTION: 4
Your database instance is configured with automatic undo management and the UNDO_RETENTION parameter is set to 900 seconds. You executed the following command to enable retention guarantee:
SQL> ALTER TABLESPACE undotbs1 RETENTION GUARANTEE;
What affect would this command have on the database?
A. The extents containing committed undo in the undo tablepace are transferred to Flash Recovery Area before being overwritten
B. The extents containing committed undo in the undo tablespace are not overwritten for at least 15 minutes
C. The extents in the undo tablespace retain data until the next full database backup
D. The extents containing committed data in the undo tablespace are not overwritten until the instance is shut down
Answer: B