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 are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Scale and Reduce sampling mode.
Does the solution meet the goal?

A. Yes

B. No












 

Suggested Answer: B

Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
Incorrect Answers:
Common data tasks for the Scale and Reduce sampling mode include clipping, binning, and normalizing numerical values.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
 https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/data-transformation-scale-and-reduce

This question is in DP-100 Exam
For getting Microsoft Azure Data Scientist Associate Certificate


Disclaimers:
The website is not related to, affiliated with, endorsed or authorized by Microsoft. 
The website does not contain actual questions and answers from Microsoft's Certification Exams.
Trademarks, certification & product names are used for reference only and belong to Microsoft.