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| Section | Objectives |
|---|---|
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace - Manage compute and data assets |
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NEW QUESTION # 476
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.
Answer:
Explanation:
Explanation
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
NEW QUESTION # 477
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.
You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298
NEW QUESTION # 478
You use the Azure Machine Learning SDK for Python v1 and notebooks to train a model. You create a compute target, an environment, and a training script by using Python code.
You need to prepare information to submit a training run.
Which class should you use?
Answer: B
Explanation:
To submit a training run in Azure Machine Learning using Python code, you should create a ScriptRunConfig object. This class defines the configuration information needed for running a training script on a compute target. The ScriptRunConfig class allows you to specify the compute target, the environment, the training script, and any script arguments or data references needed by the script.
NEW QUESTION # 479
You create an Azure Machine Learning workspace. You train a classification model by using automated machine learning (automated ML) in Azure Machine Learning studio. The training data contains multiple classes that have significantly different numbers of samples.
You must use a metric type to avoid labeling negative samples as positive and an averaging method that will minimize the class imbalance.
You need to configure the metric type and the averaging method.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION # 480
You need to select a feature extraction method.
Which method should you use?
Answer: D
Explanation:
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter τ), is a statistic used to measure the ordinal association between two measured quantities.
It is a supported method of the Azure Machine Learning Feature selection.
Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules Perform Feature Engineering Question Set 3
NEW QUESTION # 481
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