Azure Data Scientists Associate Practice Exam

In the context of a model training pipeline, the second step typically involves feeding processed data into the training phase of the model. After the data has been preprocessed in the first step, it is crucial to pass this prepared data along to ensure that the model has the necessary inputs to learn effectively.

Using 'prep_data.outputs.output_data' as input for the second step is sensible because it directly references the output generated by the data preparation phase. This output is usually formatted and structured appropriately for training the model, which is why it serves as a reliable input for this subsequent step. By using the output from the data preparation process, the training step can operate on data that has been cleaned, transformed, and made ready for modeling.

Meanwhile, the other choices do not accurately represent the correct input for the second step in the pipeline. For instance, 'pipeline_job_input' doesn't specify any particular processed data; 'train_model.outputs.model_output' refers to the results of the training process rather than the input data required for training; and 'action_data.input_data' may suggest raw or unprocessed data that hasn't gone through the necessary preparation for effective model training. Thus, 'prep_data.outputs.output_data' is the most appropriate choice to ensure that the model training