Hortonworks Apache-Hadoop-Developer Exam : Hadoop 2.0 Certification exam for Pig and Hive Developer

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

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) Partitioner
B) Writable
C) Combiner
D) WritableComparable
E) InputFormat
F) OutputFormat

2. MapReduce v2 (MRv2/YARN) is designed to address which two issues?

A) Ability to run frameworks other than MapReduce, such as MPI.
B) HDFS latency.
C) Resource pressure on the JobTracker.
D) Reduce complexity of the MapReduce APIs.
E) Standardize on a single MapReduce API.
F) Single point of failure in the NameNode.

3. What is the term for the process of moving map outputs to the reducers?

A) Combining
B) Reducing
C) Shuffling and sorting
D) Partitioning

4. In a MapReduce job, the reducer receives all values associated with same key. Which statement best describes the ordering of these values?

A) Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values.
B) The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.
C) The values are in sorted order.
D) The values are arbitrary ordered, but multiple runs of the same MapReduce job will always have the same ordering.

5. To process input key-value pairs, your mapper needs to lead a 512 MB data file in memory. What is the best way to accomplish this?

A) Place the data file in the DistributedCache and read the data into memory in the map method of the mapper.
B) Place the data file in the DataCache and read the data into memory in the configure method of the mapper.
C) Place the data file in the DistributedCache and read the data into memory in the configure method of the mapper.
D) Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure method of the mapper.

Solutions: