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Which Dataset Attributes Generate the Most Meaningful Insights in Hadoop MapReduce?
Discover which dataset attributes are most frequently used to generate meaningful insights in MapReduce. Learn why age, education, income, and marital status are essential for Big Data demographic analysis in Hadoop certifications.
Question
Which dataset attribute was frequently used to generate meaningful insights in MapReduce?
A. Vehicle identification numbers
B. Usernames and passwords
C. Age, education, income, and marital status
D. Weather conditions and temperature
Answer
C. Age, education, income, and marital status
Explanation
In the demographic and socioeconomic use cases commonly taught in Big Data and Hadoop MapReduce certifications, attributes like age, education, income, and marital status are fundamentally relied upon to generate meaningful insights. These specific demographic variables are heavily utilized because they allow analysts to group, filter, and aggregate populations to reveal hidden trends, such as identifying financially vulnerable groups (like divorced women over 45), calculating targeted poverty metrics, or observing employment disparities across education levels. While MapReduce can technically process any data type, options A, B, and D describe attributes related to automotive tracking, cybersecurity, and meteorology, respectively, which do not align with the core demographic profiling and policy-making use cases highlighted in this specific exam context.