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NEW QUESTION: 1
A company plans to use Power BI to visualize data from business systems.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: Yes
The following list provides advantages to using aggregations:
Query performance over big data - as users interact with visuals on Power BI reports, DAX queries are submitted to the dataset. Boost query speeds by caching data at the aggregated level, using a fraction of the resources required at the detail level. Unlock big data in a way that would otherwise be impossible.
Box 2: No
You can connect to model-driven apps in Dynamics 365 with Power BI Desktop to create custom reports and dashboards for use with the Power BI service.
You can use similar steps to connect to model-driven apps in Dynamics 365 using Office Excel Power Query by selecting From Other Sources on the Power Query tab in Excel.
Box 3: Yes
Using the PowerApps custom visual, you will be able to pass context aware data to a PowerApps app which updates in real time as you make changes to your report.
Reference:
https://docs.microsoft.com/en-us/power-bi/desktop-aggregations
https://docs.microsoft.com/en-us/power-platform/admin/use-power-bi
https://powerapps.microsoft.com/sv-se/blog/powerbi-powerapps-visual/

NEW QUESTION: 2
You have the following table:

You create this trigger:

What are the results in the EMP_LOG table?

A. picture
B. picture
C. picture
D. No rows are inserted.
Answer: B

NEW QUESTION: 3
You use Azure Stream Analytics to receive Twitter data from Azure Event Hubs and to output the data to an Azure Blob storage account.
You need to output the count of tweets during the last five minutes every five minutes. Each tweet must only be counted once.
Which windowing function should you use?
A. a five-minute Tumbling window
B. a five-minute Session window
C. a five-minute Hopping window that has one-minute hop
D. a five-minute Sliding window
Answer: A
Explanation:
Explanation
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.

References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions