Exam Name: | Designing and Implementing a Data Science Solution on Azure | ||
Exam Code: | DP-100 Dumps | ||
Vendor: | Microsoft | Certification: | Microsoft Azure |
Questions: | 422 Q&A's | Shared By: | deacon |
You manage an Azure Machine learning workspace.
You build a custom model you must log with Mlftow. The custom model includes the following:
• The model is not natively supported by Mlflow.
• The model cannot be serialized in Pickle format.
• The model source code is complex.
• The Python library tor the model must be packaged with the model.
You need to create a custom model flavor to enable logging with ML. flow.
What should you use?
You use the Azure Machine Learning service to create a tabular dataset named training.data. You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get("training_data")
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training.data dataset
Which property should you set?
A)
B)
C)
D)
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:
You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
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?
A)
B)
C)
D)