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

1. 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) There is no difference in output between the two settings.
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) 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.
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.

2. Indentify the utility that allows you to create and run MapReduce jobs with any executable or script as the
mapper and/or the reducer?

A) Sqoop
B) Flume
C) mapred
D) Hadoop Streaming
E) Oozie

3. You want to perform analysis on a large collection of images. You want to store this data in HDFS and
process it with MapReduce but you also want to give your data analysts and data scientists the ability to
process the data directly from HDFS with an interpreted high-level programming language like Python.
Which format should you use to store this data in HDFS?

A) CSV
B) JSON
C) SequenceFiles
D) Avro
E) XML
F) HTML

4. 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 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.
C) When the signature of the reduce method matches the signature of the combine method.
D) Always. The point of a combiner is to serve as a mini-reducer directly after the map phase to increase performance.
E) Never. Combiners and reducers must be implemented separately because they serve different purposes.

5. What does Pig provide to the overall Hadoop solution?

A) C++ interface to MapReduce and data warehouse infrastructure
B) Database table and storage management services
C) Legacy language Integration with MapReduce framework
D) Simple scripting language for writing MapReduce programs

Solutions: