Exam DP-100 topic 2 question 31 discussion

Actual exam question from Microsoft's DP-100

Question #: 31
Topic #: 2

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

Show Suggested Answer

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

by Tehseen at June 24, 2021, 11:51 a.m.

Comments

Submit Cancel

azurelearner666

Highly Voted 2 years, 9 months ago

Response is correct, B. The "Scale and Reduce sampling mode" will not be able to compensate for the class imbalance, so B (no) is the right Answer. The proper response should be SMOTE. More info on https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/smote

upvoted 7 times

...