DP-203 Certification Practice Question #136

Question

You are building an Azure Data Factory solution to process data received from Azure Event Hubs, and then ingested into an Azure Data Lake Storage Gen2 container.
The data will be ingested every five minutes from devices into JSON files. The files have the following naming pattern.
/{deviceType}/in/{YYYY}/{MM}/{DD}/{HH}/{deviceID}_{YYYY}{MM}{DD}HH}{mm}.json
You need to prepare the data for batch data processing so that there is one dataset per hour per deviceType. The solution must minimize read times.
How should you configure the sink for the copy activity? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
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Accepted Answer

The target state is one dataset per hour per device type, so the sink must write a single hourly file named by hour and device type and use merge behavior to collapse the multiple five-minute files. Because this is a window-based ingestion pattern, `@trigger().outputs.windowStartTime` is the strongest choice for aligning the sink path to the hourly batch window. Confidence is slightly reduced only because the forum shows some wording-driven disagreement over `startTime` versus `windowStartTime`.

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