A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker. The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs. Which solutions will meet these requirements? (Select TWO.)

A: Amazon Redshift ML allows users to create, train, and deploy machine learning models using simple SQL commands directly within their Redshift data warehouse. It leverages Amazon SageMaker behind the scenes. This is a direct way to generate predictions and recommendations (such as for inventory) using data stored in Redshift. B: Amazon Redshift can invoke external AWS Lambda functions via user-defined functions (UDFs). This Lambda function can then call the pre-existing real-time SageMaker endpoint. This pattern allows users to enrich data in Redshift with real-time predictions from a remote model using standard SQL, which meets the requirement for real-time recommendations.

C. Redshift ML is designed to bring machine learning to the data within Redshift, not to schedule data exports for offline training.
D. SageMaker Autopilot is a tool for automated machine learning (building models), not for creating dashboards. Dashboards are built with BI tools like Amazon QuickSight.
E. Amazon Redshift is an analytical data warehouse, not a file storage system for archiving reports. Amazon S3 is the appropriate service for this purpose.

1. Amazon Redshift Database Developer Guide. (n.d.). Using machine learning in Amazon Redshift. AWS Documentation. Retrieved from https://docs.aws.amazon.com/redshift/latest/dg/machine-learning-in-aws-redshift.html. It states, "With Amazon Redshift ML, you can use standard SQL to create and train Amazon SageMaker models from your data in Amazon Redshift."

2. Amazon Redshift Database Developer Guide. (n.d.). Invoking a SageMaker endpoint from Amazon Redshift. AWS Documentation. Retrieved from https://docs.aws.amazon.com/redshift/latest/dg/rCREATEMODELsagemaker.html. This section describes how to "create a model in Amazon Redshift that is a reference to a model in SageMaker" to make predictions using SQL.

3. Amazon Redshift Database Developer Guide. (n.d.). Creating a scalar Lambda UDF. AWS Documentation. Retrieved from https://docs.aws.amazon.com/redshift/latest/dg/udf-creating-a-lambda-sql-udf.html. This explains the mechanism for invoking external services like SageMaker via Lambda from a SQL query.