The solution fails because it attempts to pass data between steps using a Pandas DataFrame (data_output = pd.read_csv(...)), which resides in local memory. In an Azure Machine Learning pipeline, steps typically run on remote compute targets (like aml_compute). To pass data between these isolated steps, you must use a PipelineData object (v1) or Output/Input types (v2). The current code treats a local variable as a pipeline output, which is not supported; the training step will not have access to the data processed by the first step. Additionally, PythonScriptStep expects a list of OutputFileDatasetConfig or PipelineData for the outputs parameter, not a DataFrame object.

A. Yes This is incorrect because a Pandas DataFrame cannot act as a data bridge between remote pipeline steps; it lacks the necessary metadata and persistence to be serialized across compute nodes.

Official Vendor Documentation: Microsoft Azure Machine Learning SDK v1 - PipelineData Class. Link: Microsoft Learn - PipelineData

Official Vendor Documentation: How to move data in machine learning pipeline steps (Python). Section: "Use PipelineData for intermediate data." Link: Microsoft Learn - Moving Data

Official Vendor Documentation: PythonScriptStep Class Reference. Parameters: outputs and inputs. Link: Microsoft Learn - PythonScriptStep