Gathering Real-Time Telemetry Data

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The type of workload that describes the scenario of gathering real-time telemetry data from a mobile application is "streaming," which is the correct answer from the provided options.

Streaming workload is used when there is a need to process, store, and analyze real-time data from various sources, such as IoT devices, social media feeds, and other streaming data sources. This type of workload is common in scenarios where data needs to be processed and analyzed in real-time to make immediate decisions or respond to events.

In the scenario described in the question, the mobile application is generating real-time telemetry data, which means that data is being continuously produced and needs to be analyzed in real-time to gain insights into user behavior or application performance. By using streaming technologies, such as Azure Stream Analytics or Apache Kafka, the data can be ingested, processed, and stored in real-time to provide immediate insights and actions.

The other options are not suitable for this scenario. OLTP is used for online transaction processing and is generally used for small, frequent transactions. Batch processing is used for processing large volumes of data in a batch or at a scheduled time, while MPP is used for processing large data sets in parallel. These options are not suitable for real-time telemetry data processing from a mobile application.