In a MapReduce job, you want each of your 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?

Which process describes the lifecycle of a Mapper?

Determine which best describes when the reduce method is first called in a MapReduce job?

To process input key-value pairs, your mapper needs to lead a 512 MB data file in memory. What
is the best way to accomplish this?

You have written a Mapper which invokes the following five calls to the OutputColletor.collect
method:
output.collect (new Text (“Apple”), new Text (“Red”) ) ;
output.collect (new Text (“Banana”), new Text (“Yellow”) ) ;
output.collect (new Text (“Apple”), new Text (“Yellow”) ) ;
output.collect (new Text (“Cherry”), new Text (“Red”) ) ;
output.collect (new Text (“Apple”), new Text (“Green”) ) ;
How many times will the Reducer’s reduce method be invoked?

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

You need to create a job that does frequency analysis on input data. You will do this by writing a
Mapper that uses TextInputFormat and splits each value (a line of text from an input file) into
individual characters. For each one of these characters, you will emit the character as a key and
an InputWritable as the value. As this will produce proportionally more intermediate data than input
data, which two resources should you expect to be bottlenecks?

Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop
daemon on which the Hadoop framework will look for an available slot schedule a MapReduce
operation.

You want to count the number of occurrences for each unique word in the supplied input data.
You’ve decided to implement this by having your mapper tokenize each word and emit a literal
value 1, and then have your reducer increment a counter for each literal 1 it receives. After
successful implementing this, it occurs to you that you could optimize this by specifying a
combiner. Will you be able to reuse your existing Reduces as your combiner in this case and why
or why not?

Which project gives you a distributed, Scalable, data store that allows you random, realtime
read/write access to hundreds of terabytes of data?