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NEW QUESTION: 1

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

NEW QUESTION: 2

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

NEW QUESTION: 3
What kind of workload is MapReduce designed to handle?
A. computational
B. interactive
C. batch processing
D. real time
E. commodity
Answer: C
Explanation:
Hadoop was designed for batch processing. That means, take a large dataset in input all at once, process it, and write a large output. The very concept of MapReduce is geared towards batch and not real-time. With growing data, Hadoop enables you to horizontally scale your cluster by adding commodity nodes and thus keep up with query. In hadoop
Map-reduce does the same job it will take large amount of data and process it in batch. It will not give immediate output. It will take time as per Configuration of system,namenode,task-tracker,job-tracker etc.
References: https://www.quora.com/What-is-batch-processing-in-hadoop

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