Exam DP-100 topic 3 question 111 discussion

as_input is for next step in the pipeline; register_on_complete is for dataset register after the pipeline run ends

B. reference: https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.output_dataset_config.outputdatasetconfig?view=azure-ml-py#methods

Yeah Option B is correct.

B To register the output of an Azure Machine Learning pipeline as a new version of a named dataset after the run has been completed, you should implement option B: B. the register_on_complete method of the OutputDatasetConfig class The register_on_complete method allows you to register the output dataset as a new version of an existing named dataset after the pipeline run is completed. This is the appropriate method for creating a new version of a named dataset in Azure Machine Learning.

Seriously, is anyone checking this answers? There are so many obvious mistakes.

B https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.output_dataset_config.outputdatasetconfig?view=azure-ml-py#azureml-data-output-dataset-config-outputdatasetconfig-register-on-complete

# Create an OutputFileDatasetConfig output = OutputFileDatasetConfig(destination=(datastore, 'output/{run-id}')) # Register the output as a new version of a named Dataset after the run has completed output.register_on_complete('my-dataset') # Create a ScriptRunConfig script_run_config = ScriptRunConfig('.', 'train.py', arguments=[output])

B. The register_on_complete method of the OutputDatasetConfig class should be implemented to register the output of the pipeline as a new version of a named dataset after the run has been completed.

Correct answer is B: https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.output_dataset_config.outputdatasetconfig?view=azure-ml-py#azureml-data-output-dataset-config-outputdatasetconfig-register-on-complete