Batch endpoints are specifically designed for high-throughput, asynchronous scoring of large volumes of data. They work by accepting a reference to the data, initiating a job that runs over time, and storing the outputs for later retrieval. This pattern is ideal for scenarios where immediate, low-latency responses are not required, such as processing daily transaction data or scoring large image datasets.

A. A Real-time endpoint is used for synchronous, low-latency inference on small payloads, not large asynchronous jobs.

C. An Online endpoint is the current terminology for a real-time endpoint and serves the same synchronous purpose.

D. Streaming endpoints are for processing continuous data streams, which is a different pattern than batch processing.

1. Microsoft. (2023). What are Azure Machine Learning endpoints?. Microsoft Learn. Retrieved from https://docs.microsoft.com/en-us/azure/machine-learning/concept-endpoints. (Section: "What are endpoints?")

2. Microsoft. (2023). Use batch endpoints for batch scoring. Microsoft Learn. Retrieved from https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-endpoint. (Section: "What are batch endpoints?")