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NEW QUESTION: 1

A. Option A
B. Option B
C. Option D
D. Option C
Answer: A

NEW QUESTION: 2



A. Option A
B. Option C
C. Option B
D. Option D
Answer: B

NEW QUESTION: 3
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 Learning learning Studio.
One class has a much smaller number of observations than the other classes in the training You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
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.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION: 4
DatacapとBusinessAutomation Content Analyzerの統合を担当する主要なコンポーネントは何ですか?
A. Content Analyzer connector
B. Content Analyzer actions
C. Datacap actions
D. Datacap connector
Answer: A

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