Azure Stream Analytics is correct because it is designed for low latency processing of continuous event streams and it provides built in machine learning scoring capabilities.

Azure Stream Analytics is a managed real time analytics service that ingests events from sources such as Event Hubs and IoT Hub and executes SQL like queries with windowing to produce low latency results. It supports real time scoring by calling Azure Machine Learning endpoints and by using built in functions that apply models to streaming data so you can do machine learning inference as part of the streaming pipeline.

Azure Synapse Analytics is incorrect. Synapse focuses on large scale data warehousing and integrated analytics across batch and interactive workloads rather than low latency continuous event stream processing and built in streaming ML scoring.

Azure Databricks is incorrect. Databricks offers a unified analytics platform based on Apache Spark and it can process streams and run machine learning pipelines, but it is aimed at notebooks and heavy data engineering workloads and it does not provide the same managed low latency streaming service with built in scoring out of the box.

Azure Machine Learning is incorrect. Azure Machine Learning is focused on training, managing and deploying models and it provides endpoints for scoring, but it is not itself a managed stream processing engine for low latency continuous event streams.

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