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| Section | Objectives |
|---|---|
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Manage compute and data assets - Plan and configure Azure Machine Learning workspace |
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NEW QUESTION # 359
You use Azure Machine Learning Designer to load the following datasets into an experiment:
Dataset1
Dataset2
You use Azure Machine Learning Designer to load the following datasets into an experiment:
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Join Data component.
Does the solution meet the goal?
Answer: B
NEW QUESTION # 360
You are producing a multiple linear regression model in Azure Machine learning Studio.
Several independent variables are highly correlated.
You need to select appropriate methods for conducting elective feature engineering on all the data.
Which three actions should you perform in sequence? To answer, move the appropriate Actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 361
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps. Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
NEW QUESTION # 362
You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint. You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.
Solution: Create a data asset in the workspace.
Does the solution meet the goal?
Answer: B
NEW QUESTION # 363
You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.
You submit the experiment with the following code:
You need to create Python code that returns the best model that is generated by the automated machine learning task. Which code segment should you use?
Answer: D
Explanation:
The get_output method returns the best run and the fitted model.
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-mach
NEW QUESTION # 364
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