Note: This question is part of a series of questions that present the same scenario.

This solution attempts to pass the dataset filename as a positional argument, bypassing the entire Azure Machine Learning input-binding mechanism. In Azure ML command jobs, data assets are not simple local file paths — they are managed references to data stored in Azure Storage that Azure ML resolves and mounts or downloads to the compute target at runtime. The script cannot directly reference dataset1.csv as a bare filename because there is no guarantee that file will be in the working directory of the compute target. Moreover, the script ' s argparse configuration uses --training_data as a named parameter, not a positional argument, so the command syntax does not match the expected interface. The correct approach uses the input binding placeholder syntax, which instructs Azure ML to resolve the registered data asset and provide its local path to the script at runtime.

Microsoft Learn Reference Topic: Work with data in Azure Machine Learning command jobs – Input binding syntax