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

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

2. Assuming the following Hive query executes successfully:

Which one of the following statements describes the result set?

A) A bigram of the top 80 sentences that contain the substring "you are" in the lines column of the input
data A1 table.
B) A frequency distribution of the top 80 words that follow the subsequence "you are" in the lines column
of the inputdata 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.

3. 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 that spill over to the local file system of the TaskTracker node running the
Mapper.
B) Into in-memory buffers on the TaskTracker node running the Reducer 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 on the TaskTracker node running the Mapper that spill over and are written into
HDFS.
E) Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node
running the Reducer

4. How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce?

A) Keys are presented to reducer in sorted order; values for a given key are sorted in ascending order.
B) Keys are presented to a reducer in random order; values for a given key are not sorted.
C) Keys are presented to reducer in sorted order; values for a given key are not sorted.
D) Keys are presented to a reducer in random order; values for a given key are sorted in ascending order.

5. Which Hadoop component is responsible for managing the distributed file system metadata?

A) DataNode
B) NameNode
C) Metanode
D) NameSpaceManager

Solutions: