Microsoft Updated DP-100 Exam Questions and Answers by daniella

The get_output method on automl_classifier returns the best run and the fitted model for the last invocation. Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration.

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best_run, fitted_model = local_run.get_output()

Step 1: Publish the pipeline.

To schedule a pipeline, you'll need a reference to your workspace, the identifier of your published pipeline, and the name of the experiment in which you wish to create the schedule.

Step 2: Retrieve the pipeline ID.

Needed for the schedule.

Step 3: Create a ScheduleRecurrence..

To run a pipeline on a recurring basis, you'll create a schedule. A Schedule associates a pipeline, an experiment, and a trigger.

First create a schedule. Example: Create a Schedule that begins a run every 15 minutes:

recurrence = ScheduleRecurrence(frequency="Minute", interval=15)

Step 4: Define an Azure Machine Learning pipeline schedule..

Example, continued:

recurring_schedule = Schedule.create(ws, name="MyRecurringSchedule",

description="Based on time",

pipeline_id=pipeline_id,

experiment_name=experiment_name,

recurrence=recurrence)

Box 1: Yes

Parameter source_directory is a local directory containing experiment configuration and code files needed for a training job.

Box 2: Yes

script_params is a dictionary of command-line arguments to pass to the training script specified in entry_script.

Box 3: No

Box 4: Yes

The conda_packages parameter is a list of strings representing conda packages to be added to the Python environment for the experiment.