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What is the primary advantage of using PySpark for data processing in Microsoft Fabric?

You have a Fabric workspace that contains a Real-Time Intelligence solution and an eventhouse.

Users report that from OneLake file explorer, they cannot see the data from the eventhouse.

You enable OneLake availability for the eventhouse.

What will be copied to OneLake?

Which feature in Microsoft Fabric simplifies the integration of various data sources?

Which storage format is best suited for optimizing read performance in Microsoft Fabric?

Note: This question is part of a series of questions that present the same scenario. Each question in the series

contains a unique solution that might meet the stated goals. Some question sets might have more than one

correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions

will not appear in the review screen.

You have a Fabric eventstream that loads data into a table named Bike_Location in a KQL database. The

table contains the following columns:

BikepointID

Street

Neighbourhood

No_Bikes 

No_Empty_Docks

Timestamp

You need to apply transformation and filter logic to prepare the data for consumption. The solution must

return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered

by No_Bikes in ascending order.

Solution: You use the following code segment:

Does this meet the goal?