You create an Azure Machine Learning pipeline named pipeline1 that has two steps containing Python scripts. The first step passes processed data to the second step.

You update the content of the downstream data source for pipeline1 and must run the pipeline again. You need to ensure the new run of pipeline1 fully processes the updated content.

Solution: You set the regenerate_outputs parameter of the run submit method for the pipeline1 experiment to True.

Does the solution meet the goal?