DP-203 Azure Data Engineer Associate Real-Time Analytics and Streaming Flashcards
6 cards from real DP-203 Azure Data Engineer Associate practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
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Which windowing function in Azure Stream Analytics produces overlapping windows that slide forward by a defined step interval?
Answer: Hopping Window
Hopping Windows have a fixed size but slide forward by a hop interval that is shorter than the window size, resulting in overlapping windows where an event can appear in multiple windows.
In Azure Event Hubs, what determines the maximum number of parallel consumers reading from an Event Hub?
Answer: The number of partitions
The number of partitions in an Event Hub sets the upper bound on parallelism because each partition can only be read by one consumer at a time within a Consumer Group.
Which Azure Databricks feature enables structured streaming of data from Azure Event Hubs?
Answer: Spark Structured Streaming with Event Hubs connector
Spark Structured Streaming in Databricks, combined with the Azure Event Hubs connector library, allows you to consume event streams as a continuous DataFrame.
What is Azure IoT Hub's primary advantage over Azure Event Hubs for IoT scenarios?
Answer: Bidirectional communication with devices and per-device identity management
Azure IoT Hub supports bidirectional communication (device-to-cloud and cloud-to-device), per-device authentication, and device twin state management, which Event Hubs does not offer.
In Azure Stream Analytics, which output type allows writing results to a Power BI dataset for real-time dashboards?
Answer: Power BI
Stream Analytics supports Power BI as a native output, enabling the query results to be pushed directly to a Power BI streaming dataset for real-time visualization.
What does 'checkpointing' accomplish in Spark Structured Streaming?
Answer: It saves the streaming query progress to fault-tolerant storage to enable recovery after failure
Checkpointing in Spark Structured Streaming periodically saves the query's progress metadata and state to a fault-tolerant location (e.g., ADLS) so the stream can resume without data loss after a restart.
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