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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:

Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:

Question 1

You've written a MapReduce job that will process 500 million input records and generated 500 million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reduces which is a potential bottleneck. A custom implementation of which interface is most likely to reduce the amount of intermediate data transferred across the network?

A. InputFormat
B. WritableComparable
C. Combiner
D. OutputFormat
E. Partitioner
F. Writable

Question 2

Which one of the following statements is true about a Hive-managed table?

A. Records can only be added to the table using the Hive INSERT command.
B. When the table is dropped, the underlying folder in HDFS is deleted.
C. Hive dynamically defines the schema of the table based on the format of the underlying data.
D. Hive dynamically defines the schema of the table based on the FROM clause of a SELECT query.

Question 3

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

A. Always. The point of a combiner is to serve as a mini-reducer directly after the map phase to increase performance.
B. When the signature of the reduce method matches the signature of the combine method.
C. Always. Code can be reused in Java since it is a polymorphic object-oriented programming language.
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.

Question 4

Review the following data and Pig code:

What command to define B would produce the output (M,62,95l02) when invoking the DUMP operator on B?

A. B = FILTER A BY (zip = = '95102' AND gender = = M");
B. B= GROUP A BY (zip = = '95102' AND gender = = 'M');
C. B= FOREACH A BY (gender = = 'M' AND zip = = '95102');
D. B = JOIN A BY (gender = = 'M' AND zip = = '95102');

Question 5

You have user profile records in your OLPT database, that you want to join with web logs you have already ingested into the Hadoop file system. How will you obtain these user records?

A. HDFS command
B. Hive LOAD DATA command
C. Sqoop import
D. Pig LOAD command
E. Ingest with Flume agents
F. Ingest with Hadoop Streaming

Solutions: