Question
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Explanation
Low-latency synchronous predictions: a real-time (managed online) endpoint serves requests over HTTPS and returns predictions immediately, which suits calls made during customer interactions. A batch endpoint is asynchronous, a training pipeline does not serve predictions, and a registered model artifact is only the stored model, not an inference option.
High-volume scheduled scoring: a batch endpoint runs asynchronous jobs over large files in parallel on a compute cluster and writes the results to storage, and it can be invoked on a schedule. An online endpoint with autoscaling is built for request-response traffic, not file-based scoring of millions of records; a compute cluster is only the compute a batch deployment runs on, and a model registry version is not an inference option.
