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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) 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) Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as
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. You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses
TextInputFormat: the mapper applies a regular expression over input values and emits key-values pairs
with the key consisting of the matching text, and the value containing the filename and byte offset.
Determine the difference between setting the number of reduces to one and settings the number of
reducers to zero.

A) With zero reducers, all instances of matching patterns are gathered together in one file on HDFS. With
one reducer, instances of matching patterns are stored in multiple files on HDFS.
B) There is no difference in output between the two settings.
C) With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one
reducer, all instances of matching patterns are gathered together in one file on HDFS.
D) With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances of
matching patterns are stored in a single file on HDFS.

3. 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

4. What is the disadvantage of using multiple reducers with the default HashPartitioner and distributing your
workload across you cluster?

A) You will longer be able to take advantage of a Combiner.
B) By using multiple reducers with the default HashPartitioner, output files may not be in globally sorted
order.
C) You will not be able to compress the intermediate data.
D) There are no concerns with this approach. It is always advisable to use multiple reduces.

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

Solutions: