Full Access Microsoft DP-100 Tutorials

You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using the following code:

from azureml.pipeline.core import Pipeline

from azureml.core.experiment import Experiment

pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])

pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)

You need to monitor the progress of the pipeline execution.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary

classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.

You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.

Which two hyperparameters should you use? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.

You must meet the following requirements:

• Reduce the number of training epochs.

• Reduce the size of the neural network.

• Reduce over-fitting of the neural network.

You need to select the image modification values.

Which value should you use? To answer, select the appropriate Options in the answer area.

NOTE: Each correct selection is worth one point.

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'