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

1. 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.

2. Consider the following two relations, A and B.

Which Pig statement combines A by its first field and B by its second field?

A) C = DOIN B BY a1, A by b2;
B) C = JOIN A a1, B b2;
C) C = JOIN A SO, B $1;
D) C = JOIN A by al, B by b2;

3. 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) OutputFormat
C) Combiner
D) Writable
E) Partitioner
F) WritableComparable

4. Identify the tool best suited to import a portion of a relational database every day as files into HDFS, and generate Java classes to interact with that imported data?

A) Flume
B) Hue
C) Oozie
D) Hive
E) fuse-dfs
F) Pig
G) Sqoop

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

Solutions: