How should you configure the solution?

HOTSPOT
You have an Azure Storage account that generates 200,000 new files daily. The file names have a format of {YYYY}/{MM}/{DD}/{HH}/{CustomerID}.csv.
You need to design an Azure Data Factory solution that will load new data from the storage account to an Azure Data Lake once hourly. The solution must minimize load times and costs.
How should you configure the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Hot Area:

Correct Answer:

Explanation/Reference:
Explanation:
Box 1: Incremental load
When you start to build the end to end data integration flow the first challenge is to extract data from different data stores, where incrementally (or delta) loading data after an initial full load is widely used at this stage. Now, ADF provides a new capability for you to incrementally copy new or changed files only by LastModifiedDate from a file-based store. By using this new feature, you do not need to partition the data by time-based folder or file name. The new or changed file will be automatically selected by its metadata LastModifiedDate and copied to the destination store.
Box 2: Tumbling window
Tumbling window triggers are a type of trigger that fires at a periodic time interval from a specified start time, while retaining state. Tumbling windows are a series of fixed-sized, non-overlapping, and contiguous time intervals. A tumbling window trigger has a one-to-one relationship with a pipeline and can only reference a singular pipeline.
Reference:
https://azure.microsoft.com/en-us/blog/incrementally-copy-new-files-by-lastmodifieddate-with-azure-data-factory/
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger