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Hortonworks HADOOP-PR000007 Exam Syllabus Topics:

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

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

2. Examine the following Pig commands:

Which one of the following statements is true?

A) The reducers will only output the first 20% of the data passed from the mappers
B) Each MapReduce task will terminate after executing for 0.2 minutes
C) A random sample of approximately 20% of the data will be output
D) The SAMPLE command generates an "unexpected symbol" error

3. What is a SequenceFile?

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

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

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

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

Solutions: