- (Exam Topic 3)
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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?

1. A. Yes
2. B. No

Correct Answer: B
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder, compute_target=compute_target, entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn

- (Exam Topic 3)
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.

1. A. a choice expression for learning_rate
2. B. a uniform expression for learning_rate
3. C. a normal expression for batch_size
4. D. a choice expression for batch_size
5. E. a uniform expression for batch_size

Correct Answer: BD
B: Continuous hyperparameters are specified as a distribution over a continuous range of values. Supported distributions include:
uniform(low, high) - Returns a value uniformly distributed between low and high
D: Discrete hyperparameters are specified as a choice among discrete values. choice can be:
one or more comma-separated values
a range object
any arbitrary list object Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

- (Exam Topic 3)
You are planning to register a trained model in an Azure Machine Learning workspace.
You must store additional metadata about the model in a key-value format. You must be able to add new metadata and modify or delete metadata after creation.
You need to register the model. Which parameter should you use?

1. A. description
2. B. model_framework
3. C. cags
4. D. properties

Correct Answer: D
azureml.core.Model.properties:
Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model

- (Exam Topic 3)
You are developing a machine learning, experiment by using Azure. The following images show the input and output of a machine learning experiment:

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Solution:


Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 3)
You are using the Hyperdrive feature in Azure Machine Learning to train a model. You configure the Hyperdrive experiment by running the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Solution:
Box 1: Yes
In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Box 2: Yes
learning_rate has a normal distribution with mean value 10 and a standard deviation of 3.
Box 3: No
keep_probability has a uniform distribution with a minimum value of 0.05 and a maximum value of 0.1.
Box 4: No
number_of_hidden_layers takes on one of the values [3, 4, 5].
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A