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| Section | Objectives |
|---|---|
| Topic 1: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 2: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 3: 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 |
| Topic 4: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Topic 5: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
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NEW QUESTION # 488
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 an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.6 - AzureML kernel.
Does the solution meet the goal?
Answer: A
NEW QUESTION # 489
Hotspot Question
You collect data from a nearby weather station. You have a pandas dataframe named weather_df that includes the following data:
The data is collected every 12 hours: noon and midnight.
You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.
You must use the Azure Machine Learning SDK v2 to run an automated machine learning experiment to train these models.
You need to configure the automated machine learning job and its settings.
How should you configure parameters of the classes that implement the job? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 490
You train classification and regression models by using automated machine learning.
You must evaluate automated machine learning experiment results. The results include how a classification model is making systematic errors in its predictions and the relationship between the target feature and the regression model's predictions. You must use charts generated by automated machine learning.
You need to choose a chart type for each model type.
Which chart types should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
See below image
NEW QUESTION # 491
You use an Azure Machine Learning workspace.
You create the following Python 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.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.scriptrunconfig
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment
NEW QUESTION # 492
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 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 Add Rows module.
Does the solution meet the goal?
Answer: A
NEW QUESTION # 493
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