A machine learning engineer needs to select a deployment strategy for a new machine learning...

Real-time deployment is a type of model deployment where predictions are computed on-demand at query time. This can be useful when the feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time. Real-time deployment can leverage Databricks Model Serving, which allows users to host MLflow models as REST endpoints on Databricks1. The other options are incorrect because they either do not meet the requirements or are not supported by Databricks Machine Learning. Edge/on-device deployment is a type of model deployment where predictions are computed on the edge device, such as a smartphone or a sensor. This can be useful when there is limited or no network connectivity, or when privacy and security are important. However, edge deployment is not supported by Databricks Machine Learning2. Streaming deployment is a type of model deployment where predictions are computed on streaming data, such as sensor readings or web logs. This can be useful when the data volume is high and the results need to be updated continuously. However, streaming deployment is not supported by Databricks Model Serving1. Batch deployment is a type of model deployment where predictions are computed in advance and stored for later use. This can be useful when the feature values are known ahead of time and the predictions do not need to be delivered quickly. However, batch deployment does not meet the requirements of this scenario, as the feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time. References: MLflow Model Serving, Model deployment patterns