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Hortonworks HADOOP-PR000007 Exam Syllabus Topics:

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

1. Which one of the following statements is true regarding a MapReduce job?

A) The reduce method is invoked once for each unique value
B) The Mapper must sort its output of (key.value) pairs in descending order based on value
C) The default Hash Partitioner sends key value pairs with the same key to the same Reducer
D) The job's Partitioner shuffles and sorts all (key.value) pairs and sends the output to all reducers

2. The Hadoop framework provides a mechanism for coping with machine issues such as faulty
configuration or impending hardware failure. MapReduce detects that one or a number of machines are
performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and
the task finish first are used. This is called:

A) Speculative Execution
B) IdentityMapper
C) Combine
D) Default Partitioner
E) IdentityReducer

3. What does the following WebHDFS command do?
Curl -1 -L "http://host:port/webhdfs/v1/foo/bar?op=OPEN"

A) Make a directory /foo/bar
B) Read a file /foo/bar
C) List a directory /foo
D) Delete a directory /foo/bar

4. Which best describes how TextInputFormat processes input files and line breaks?

A) Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the
split that contains the beginning of the broken line.
B) Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of
both splits containing the broken line.
C) Input file splits may cross line breaks. A line that crosses file splits is ignored.
D) Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the
split that contains the end of the broken line.
E) The input file is split exactly at the line breaks, so each RecordReader will read a series of complete
lines.

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

Solutions: