Question 1

Which Python commands should you use to create and register a tabular dataset using the from_delimited_files method of the Dataset.Tabular class?

from azureml.core import Dataset blob_ds = ws.change_default_datastore() csv_paths = [(blob_ds, 'data/files/current_data.csv'), (blob_ds, 'data/files/archive/*.csv')] tab_ds = Dataset.Tabular.from_delimited_files(path=csv_paths) tab_ds = tab_ds.register(workspace=ws, name='csv_table')

from azureml.core import Dataset blob_ds = ws.get_default_datastore() csv_paths = [(blob_ds, 'data/files/current_data.csv'), (blob_ds, 'data/files/archive/*.csv')] tab_ds = Dataset.Tabular.from_delimited_files() tab_ds = tab_ds.register(workspace=ws, name='csv_table')

from azureml.core import Dataset blob_ds = ws.get_default_datastore() csv_paths = [(blob_ds, 'data/files/current_data.csv'), (blob_ds, 'data/files/archive/csv')] tab_ds = Dataset.Tabular.from_delimited_files(path=csv_paths) tab_ds = tab_ds.register(workspace=ws, name='csv_table')

from azureml.core import Dataset blob_ds = ws.get_default_datastore() csv_paths = [(blob_ds, 'data/files/current_data.csv'), (blob_ds, 'data/files/archive/*.csv')] tab_ds = Dataset.Tabular.from_delimited_files(path=csv_paths) tab_ds = tab_ds.register(workspace=ws, name='csv_table')

Question 2

You’re creating a file dataset using the from_files method of the Dataset.File class.

You also want to register it in the workspace with the name img_files.

Which SDK commands can you use?

from azureml.core import Dataset file_ds = Dataset.File.from_files(path=(blob_ds, 'data/files/images/*.jpg')) file_ds = file_ds.register(workspace=ws, name='img_files')

from azureml.core import Dataset blob_ds = ws.get_default_datastore() file_ds = Dataset.File.from_files(path=(blob_ds, 'data/files/images/*.jpg'))

from azureml.core import Dataset blob_ds = ws.get_default_datastore() file_ds = Dataset.File.from_files(path=(blob_ds, 'data/files/images/*.jpg')) file_ds = file_ds.register(workspace=ws, name='img_files')

from azureml.core import Dataset blob_ds = ws.get_default_datastore() file_ds = Dataset.File.from_files(path=(blob_ds, 'data/files/images')) file_ds = file_ds.register(workspace=ws, name='img_files')

Question 3

What methods can you use from the Dataset class to retrieve a dataset after registering it? Select all that apply.

Question 4

To retrieve a specific version of a data set, which SDK commands should you use?

Question 5

Which SDK commands can you use to view the registered environments in your workspace?

Question 6

You are defining a compute configuration for a managed compute target using the SDK.

Which of the below commands is correct?

Question 7

You created a compute target and now you want to use it for an experiment. You want to specify the compute target using a ComputeTarget object.

Which of the SDK commands below can you use?

compute_name = "aml-cluster" training_cluster = ComputeTarget(workspace=ws, name=compute_name) training_env = Environment.get(workspace=ws, name='training_environment') script_config = ScriptRunConfig(source_directory='my_dir', script='script.py', environment=training_env, compute_target=training_cluster)

compute_name = "aml-cluster" training_cluster = ComputeTarget(workspace=ws) training_env = Environment.get(workspace=ws, name='training_environment') script_config = ScriptRunConfig(source_directory='my_dir', script='script.py', environment=training_env, compute_target=training_cluster)

compute_name = "aml-cluster" training_env = Environment.get(workspace=ws, name='training_environment') script_config = ScriptRunConfig(source_directory='my_dir', script='script.py', environment=training_env, compute_target=training_cluster)