Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You are building a Microsoft Fabric Eventstream to ingest sensor readings from Azure IoT Hub. Each event payload contains the fields deviceId (string), ts (epoch milliseconds), temperature (double), and humidity (double).
You must satisfy the following processing requirements:
Guarantee that statistics are calculated by the timestamp in each event, even if messages arrive out of order.
Discard any event that arrives more than 2 minutes after its ts value.
Produce a running 1-minute tumbling-window average of the temperature for each device and store the result in a Real-Time Analytics KQL database table.
Which configuration should you apply to the Eventstream input or query to meet all the requirements?
Mark ts as the event-time column and use a 1-minute hopping window with a 30-second hop size; do not configure out-of-order tolerance because tumbling windows implicitly drop late data.
Use the system column _arrivalTime for windowing, add a WHERE clause that filters events older than 2 minutes, and write results to the KQL table every minute.
Mark ts as the event-time column on the IoT Hub input, set a 2-minute out-of-order tolerance with the late-arrival policy set to Drop, and in the query use a 1-minute tumbling window on ts with GROUP BY deviceId.
Leave event ordering at the default arrival time, and in the query declare a 1-minute session window on ts; set the session timeout to 2 minutes to ignore late events.
Designating ts as the event-time column tells Eventstream to sequence and process data by when the sensor reading actually occurred instead of by its arrival time. You then set a 2-minute out-of-order tolerance on ts and configure the late-arrival events policy to Drop so that any record whose ts timestamp is more than two minutes older than the current watermark is discarded. Once ordering and late-arrival handling are in place, a SQL query that applies a 1-minute tumbling window on ts and groups by deviceId can safely compute AVG(temperature) per device and route the results to a KQL table. Filtering with a WHERE clause or relying on arrival time would not guarantee correct ordering or late-event removal.
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Microsoft Fabric Data Engineer Associate DP-700
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