You use the following code to define the steps for a pipeline:
from azureml.core import Workspace, Experiment, Run
from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep
ws = Workspace.from_config()
. . .
step1 = PythonScriptStep(name="step1", ...)
step2 = PythonScriptsStep(name="step2", ...)
pipeline_steps = [step1, step2]
You need to add code to run the steps.
Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

You register a model that you plan to use in a batch inference pipeline.
The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.
You need to configure the pipeline.
Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?

You create an Azure Machine Learning pipeline named pipeline 1 with two steps that contain Python scnpts.
Data processed by the first step is passed to the second step.
You must update the content of the downstream data source of pipeline 1 and run the pipeline again.
You need to ensure the new run of pipeline 1 fully processes the updated content.
Solution: Change the value of the compute.target parameter of the PythonScriptStep object in the two steps.
Does the solution meet the goal'

You train a model by using Azure Machine Learning. You use Azure Blob Storage to store production data.
The model must be re-trained when new data is uploaded to Azure Blob Storage. You need to minimize development and coding.
You need to configure Azure services to develop a re-training solution.
Which Azure services should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.