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Data Mining Multiple Choice Questions (MCQ) Online Test #2


Data Mining MCQ #11:

How does data mining contribute to sentiment analysis?

About Data Mining Multiple Choice Question (MCQ) #11:
This ICT Multiple Choice Question (ICT MCQ) #11 focuses on Data Mining within the "Advanced ICT MCQ" category. This question explains how data mining contributes to sentiment analysis.

Data Mining MCQ #12:

Which data mining technique is used to group similar data points together?

About Data Mining Multiple Choice Question (MCQ) #12:
This ICT Multiple Choice Question (ICT MCQ) #12 focuses on Data Mining within the "Advanced ICT MCQ" category. This question identifies the data mining technique used to group similar data points together.

Data Mining MCQ #13:

Which algorithm is commonly used for classifying data based on the majority vote of its k nearest neighbors?

About Data Mining Multiple Choice Question (MCQ) #13:
This ICT Multiple Choice Question (ICT MCQ) #13 focuses on Data Mining within the "Advanced ICT MCQ" category. This question identifies the algorithm commonly used for classifying data based on the majority vote of its k nearest neighbors.

Data Mining MCQ #14:

What is RapidMiner primarily used for in data mining?

About Data Mining Multiple Choice Question (MCQ) #14:
This ICT Multiple Choice Question (ICT MCQ) #14 focuses on Data Mining within the "Advanced ICT MCQ" category. This question identifies what RapidMiner is primarily used for in data mining.

Data Mining MCQ #15:

Which technique is commonly used in social media mining to identify trending topics and sentiment?

About Data Mining Multiple Choice Question (MCQ) #15:
This ICT Multiple Choice Question (ICT MCQ) #15 focuses on Data Mining within the "Advanced ICT MCQ" category. This question identifies the technique commonly used in social media mining to identify trending topics and sentiment.

Data Mining MCQ #16:

How can data mining help in enhancing customer experience in marketing?

About Data Mining Multiple Choice Question (MCQ) #16:
This ICT Multiple Choice Question (ICT MCQ) #16 focuses on Data Mining within the "Advanced ICT MCQ" category. This question identifies how data mining can enhance customer experience in marketing.

Data Mining MCQ #17:

In predictive analytics, which technique is commonly used for forecasting future sales?

About Data Mining Multiple Choice Question (MCQ) #17:
This ICT Multiple Choice Question (ICT MCQ) #17 focuses on Data Mining within the "Advanced ICT MCQ" category. This question addresses a technique commonly used in predictive analytics for forecasting future sales.

Data Mining MCQ #18:

Which technique is commonly used for forecasting future values in time series analysis?

About Data Mining Multiple Choice Question (MCQ) #18:
This ICT Multiple Choice Question (ICT MCQ) #18 focuses on Data Mining within the "Advanced ICT MCQ" category. This question identifies a commonly used technique for forecasting future values in time series analysis.

Data Mining MCQ #19:

Which process is commonly involved in knowledge discovery from data?

About Data Mining Multiple Choice Question (MCQ) #19:
This ICT Multiple Choice Question (ICT MCQ) #19 focuses on Data Mining within the "Advanced ICT MCQ" category. This question identifies a common process involved in knowledge discovery from data.

Data Mining MCQ #20:

Which data mining technique is commonly used to detect fraudulent activities?

About Data Mining Multiple Choice Question (MCQ) #20:
This ICT Multiple Choice Question (ICT MCQ) #20 focuses on Data Mining within the "Advanced ICT MCQ" category. This question identifies a data mining technique commonly used to detect fraudulent activities.
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  • This online test, titled "Data Mining Multiple Choice Questions (MCQ) Online Test #2" is designed for individuals at the advanced level and focuses on "Data Mining". It consists of 10 carefully crafted multiple choice questions (MCQs) with five options each that assess advanced knowledge and understanding of the subject matter. This test aims to help participants evaluate their grasp of key concepts related to "Data Mining".