In a MapReduce job, you want each of you input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?

A. Increase the parameter that controls minimum split size in the job configuration.
B. Write a custom MapRunner that iterates over all key-value pairs in the entire file.
C. Set the number of mappers equal to the number of input files you want to process.
D. Write a custom FileInputFormat and override the method isSplittable to always return false.

Answer: B

In a MapReduce job, the reducer receives all values associated with same key. Which statement best describes the ordering of these values?

A. The values are in sorted order.
B. The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.
C. The values are arbitrary ordered, but multiple runs of the same MapReduce job will always have the same ordering.
D. Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values.

Answer: B