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Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) Sample Questions:

1. When can a reduce class also serve as a combiner without affecting the output of a MapReduce program?

A) Always. The point of a combiner is to serve as a mini-reducer directly after the map phase to increase performance.
B) When the signature of the reduce method matches the signature of the combine method.
C) Always. Code can be reused in Java since it is a polymorphic object-oriented programming language.
D) Never. Combiners and reducers must be implemented separately because they serve different purposes.
E) When the types of the reduce operation's input key and input value match the types of the reducer's output key and output value and when the reduce operation is both communicative and associative.

2. Given the following Pig commands:

Which one of the following statements is true?

A) The grouped relation is not valid
B) The severe relation is not valid
C) The $1 variable represents the second column of data in 'my.log'
D) The $1 variable represents the first column of data in 'my.log'

3. Assuming the following Hive query executes successfully:

Which one of the following statements describes the result set?

A) A frequency distribution of the top 80 words that follow the subsequence "you are" in the lines column
of the inputdata table.
B) A bigram of the top 80 sentences that contain the substring "you are" in the lines column of the input
data A1 table.
C) An 80-value ngram of sentences that contain the words "you" or "are" in the lines column of the
inputdata table.
D) A trigram of the top 80 sentences that contain "you are" followed by a null space in the lines column of
the inputdata table.

4. You have just executed a MapReduce job. Where is intermediate data written to after being emitted from
the Mapper's map method?

A) Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into
HDFS.
B) Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into
HDFS.
C) Intermediate data in streamed across the network from Mapper to the Reduce and is never written to
disk.
D) Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node
running the Reducer
E) Into in-memory buffers that spill over to the local file system of the TaskTracker node running the
Mapper.

5. Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the
two tables are formatted as comma-separated files in HDFS.

A) No, but it can be done with either Pig or Hive.
B) Yes, but only if one of the tables fits into memory
C) Yes, so long as both tables fit into memory.
D) Yes.
E) No, MapReduce cannot perform relational operations.

Solutions: