- (Exam Topic 3)
You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan.
You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.
You need to determine the extent to which each feature in the customer data is influencing predictions. What should you do?

1. A. Enable data drift monitoring for the model and its training dataset.
2. B. Score the model against some test data with known label values and use the results to calculate a confusion matrix.
3. C. Use the Hyperdrive library to test the model with multiple hyperparameter values.
4. D. Use the interpretability package to generate an explainer for the model.
5. E. Add tags to the model registration indicating the names of the features in the training dataset.

Correct Answer: D
for your model with different test data. The steps in this section show you how to compute and visualize engineered feature importance based on your test data.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl

- (Exam Topic 3)
You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Solution:
Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Step 2: Add a dataset to the experiment
Step 3: Add a Split Data module to create training and test dataset.
To generate a set of feature scores requires that you have an already trained model, as well as a test dataset. Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset. Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the
experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-mac https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importan

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 3)
You create a machine learning model by using the Azure Machine Learning designer. You publish the model as a real-time service on an Azure Kubernetes Service (AKS) inference compute cluster. You make no changes to the deployed endpoint configuration.
You need to provide application developers with the information they need to consume the endpoint.
Which two values should you provide to application developers? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

1. A. The name of the AKS cluster where the endpoint is hosted.
2. B. The name of the inference pipeline for the endpoint.
3. C. The URL of the endpoint.
4. D. The run ID of the inference pipeline experiment for the endpoint.
5. E. The key for the endpoint.

Correct Answer: CE
Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model.
You create a web service when you deploy a model to your local environment, Azure Container Instances, Azure Kubernetes Service, or field-programmable gate arrays (FPGA). You retrieve the URI used to access the web service by using the Azure Machine Learning SDK. If authentication is enabled, you can also use the SDK to get the authentication keys or tokens.
Example:
# URL for the web service
scoring_uri = '<your web service URI>'
# If the service is authenticated, set the key or token key = '<your key or token>'
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service

- (Exam Topic 2)
You need to configure the Edit Metadata module so that the structure of the datasets match.
Which configuration options should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solution:
Box 1: Floating point
Need floating point for Median values.
Scenario: An initial investigation shows that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format.
Box 2: Unchanged
Note: Select the Categorical option to specify that the values in the selected columns should be treated as categories.
For example, you might have a column that contains the numbers 0,1 and 2, but know that the numbers actually mean "Smoker", "Non smoker" and "Unknown". In that case, by flagging the column as categorical you can ensure that the values are not used in numeric calculations, only to group data.

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (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 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:

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_list('Label Values', label_vals) Does the solution meet the goal?

1. A. Yes
2. B. No

Correct Answer: A
run.log_list log a list of values to the run with the given name using log_list. Example: run.log_list("accuracies", [0.6, 0.7, 0.87])
Note:
Data= pd.read_csv('data.csv')
Data is read into a pandas.DataFrame, which is a two-dimensional, size-mutable, potentially heterogeneous tabular data.
label_vals =data['label'].unique
label_vals contains a list of unique label values. Reference:
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class) https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html