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1. 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?

A) Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure
method of the mapper.
B) Place the data file in the DistributedCache and read the data into memory in the map method of the
mapper.
C) Place the data file in the DistributedCache and read the data into memory in the configure method of
the mapper.
D) Place the data file in the DataCache and read the data into memory in the configure method of the
mapper.

2. Examine the following Hive statements:

Assuming the statements above execute successfully, which one of the following statements is true?

A) The output is guaranteed to be a single file with all the data sorted by age
B) Each reducer generates a file sorted by age
C) The SORT BY command causes only one reducer to be used
D) The output of each reducer is only the age column

3. Indentify which best defines a SequenceFile?

A) A SequenceFile contains a binary encoding of an arbitrary number key-value pairs. Each key must be
the same type. Each value must be the same type.
B) A SequenceFile contains a binary encoding of an arbitrary number of heterogeneous Writable objects
C) A SequenceFile contains a binary encoding of an arbitrary number of WritableComparable objects, in
sorted order.
D) A SequenceFile contains a binary encoding of an arbitrary number of homogeneous Writable objects

4. 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?

A) No, because the Reducer and Combiner are separate interfaces.
B) Yes, because the sum operation is both associative and commutative and the input and output types to
the reduce method match.
C) No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
D) No, because the Combiner is incompatible with a mapper which doesn't use the same data type for
both the key and value.
E) Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as
a combiner.

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

問題與答案：