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What Is the MapReduce Process for Calculating Gender Ratios Using Keys and Counts?
Learn how MapReduce handles gender ratio calculations in Big Data Hadoop pipelines. Discover the process of using gender as a key and aggregating counts for your certification exam.
Question
How did MapReduce handle gender ratio calculation?
A. By scanning the dataset manually
B. By using gender as a key and aggregating counts
C. By ignoring gender and focusing on age
D. By replacing missing records with averages
Answer
B. By using gender as a key and aggregating counts
Explanation
MapReduce handles gender ratio calculations by leveraging its core mechanics of mapping and reducing. During the Map phase, the program reads the dataset line by line, extracts the gender field (e.g., “Male” or “Female”), and emits it as the output key, typically paired with a value of 1. The Hadoop framework then sorts and shuffles these intermediate outputs, grouping all identical gender keys together.
Finally, in the Reduce phase, the Reducer iterates through these grouped values, aggregating (summing) the counts for each gender to produce total population counts. From these aggregated totals, the final ratio or percentage can be easily calculated. Options A, C, and D describe manual scanning, ignoring the necessary attribute, or manipulating missing data, none of which reflect the actual programmatic MapReduce aggregation process.