Question 1

To register a model using a reference to the Run used to train the model, which SDK commands can you use?

from azureml.core import Model run.register_model( model_name='classification_model', model_path='outputs/model.pkl', description='A classification model')

from azureml.core import Object run.register_model( model_name='classification_model', model_path='outputs/model.pkl', description='A classification model')

from azureml.core import Model classification_model = Model.register(workspace=your_workspace, model_name='classification_model', model_path='model.pkl', description='A classification model')

from azureml.core import Object classification_model = Model.register(workspace=your_workspace, model_name='classification_model', model_path='model.pkl', description='A classification model')

Question 2

Which of the following SDK commands can you use to create a parallel run step?

parallelrun_step = ParallelRunStep( name='batch-score', parallel_run_config=parallel.run.config, inputs=[batch_data_set.as_named_input('batch_data')], output=output_dir, arguments=[], allow_reuse=True

parallelrun_step = ParallelRunStep( name='batch-score', parallel_run_config=parallel_run_config, inputs=[batch_data_set.as_named_input('batch_data')], output=output_dir, arguments=[], allow_reuse=True

parallelrun.step = ParallelRunStep( name='batch-score', parallel_run_config=parallel_run_config, inputs=[batch_data_set.as_named_input('batch_data')], output=output_dir, arguments=[], allow_reuse=True

parallelrun_step = ParallelRunStep( name='batch-score', parallel.run.config=parallel_run_config, inputs=[batch_data_set.as_named_input('batch_data')], output=output_dir, arguments=[], allow_reuse=True

Question 3

After the run of the pipeline has completed, which code can you use to retrieve the parallel_run_step.txt file from the output of the step?

Question 4

You want to define a search space for hyperparameter tuning. The batch_size hyperparameter can have the value 128, 256, or 512 and the learning_rate hyperparameter can have values from a normal distribution with a mean of 10 and a standard deviation of 3.

How can you code this in Python?

from azureml.train.hyperdrive import choice, normal param_space = { '--batch_size': choice(128, 256, 512), '--learning_rate': normal(10, 3) }

from azureml.train.hyperdrive import choice, uniform param_space = { '--batch_size': choice(128, 256, 512), '--learning_rate': uniform(10, 3) }

from azureml.train.hyperdrive import choice, normal param_space = { '--batch_size': choice(128, 256, 512), '--learning_rate': qnormal(10, 3) }

from azureml.train.hyperdrive import choice, normal param_space = { '--batch_size': choice(128, 256, 512), '--learning_rate': lognormal(10, 3) }

Question 5

How does random sampling select values for hyperparameters?

Question 6

True or False?

Bayesian sampling can be used only with choice, uniform and quniform parameter expressions, and it can be combined with an early-termination policy.

Question 7

You want to implement a median stopping policy. How can you code this in Python?