Microsoft DP-100 Practice Test - Questions Answers, Page 15

You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.

You must use Hyperdrive to try combinations of the following hyperparameter values:

learning_rate: any value between 0.001 and 0.1 batch_size: 16, 32, or 64

You need to configure the search space for the Hyperdrive experiment.

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

NOTE: Each correct selection is worth one point.

You run an automated machine learning experiment in an Azure Machine Learning workspace. Information about the run is listed in the table below:

You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run.

Which Python code segment should you use?

You have a comma-separated values (CSV) file containing data from which you want to train a classification model.

You are using the Automated Machine Learning interface in Azure Machine Learning studio to train the classification model. You set the task type to Classification.

You need to ensure that the Automated Machine Learning process evaluates only linear models.

What should you do?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

from azureml.core import Run

import pandas as pd

run = Run.get_context()

data = pd.read_csv('data.csv')

label_vals = data['label'].unique()

# Add code to record metrics here

run.complete()

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

Solution: Replace the comment with the following code:

run.upload_file('outputs/labels.csv', './data.csv')

Does the solution meet the goal?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

from azureml.core import Run

import pandas as pd

run = Run.get_context()

data = pd.read_csv('data.csv')

label_vals = data['label'].unique()

# Add code to record metrics here

run.complete()

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

Solution: Replace the comment with the following code:

run.log_table('Label Values', label_vals)

Does the solution meet the goal?

Note: This question-is part of a series of questions that present the same scenario. Each question-in the series contains a unique solution that might meet the stated goals. Some question-sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question-in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

from azureml.core import Run

import pandas as pd run = Run.get_context() data = pd.read_csv('data.csv') label_vals = data['label'].unique() # Add code to record metrics here run.complete()

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

Solution: Replace the comment with the following code:

for label_val in label_vals:

run.log('Label Values', label_val)

Does the solution meet the goal?

You are solving a classification task.

You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.

You need to configure the k parameter for the cross-validation.

Which value should you use?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You create a model to forecast weather conditions based on historical data.

You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

Solution: Run the following code:

Does the solution meet the goal?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You create a model to forecast weather conditions based on historical data.

You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

Solution: Run the following code:

Does the solution meet the goal?