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…

Question

Options

• Aexperiment = Experiment(workspace=ws,
• Brun = Run(pipeline_steps)
• Cpipeline = Pipeline(workspace=ws, steps=pipeline_steps)
• Dpipeline = Pipeline(workspace=ws, steps=pipeline_steps)

• A
  7% (4)
• B
  3% (2)
• C
  90% (52)

Explanation

Both C and D show the same first line: pipeline = Pipeline(workspace=ws, steps=pipeline_steps). This creates a Pipeline object from the defined steps. The two valid approaches to then submit and run it are: (C) using experiment.submit(pipeline) - where an Experiment object is created and the pipeline is submitted to it, returning a PipelineRun; and (D) using pipeline.submit(experiment_name='...') - directly calling submit on the Pipeline object with an experiment name. Option B (run = Run(pipeline_steps)) is invalid because the Run class constructor does not accept pipeline steps. Option A is incomplete as shown. Both C and D represent correct end-to-end patterns for executing an Azure ML pipeline.

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