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

1. Which YARN component is responsible for monitoring the success or failure of a Container?

A) ResourceManager
B) NodeManager
C) ApplicationMaster
D) JobTracker

2. Given the following Pig command:
logevents = LOAD 'input/my.log' AS (date:chararray, levehstring, code:int, message:string);
Which one of the following statements is true?

A) The logevents relation represents the data from the my.log file, using a tab as the parsing delimiter
B) The statement is not a valid Pig command
C) The logevents relation represents the data from the my.log file, using a comma as the parsing delimiter
D) The first field of logevents must be a properly-formatted date string or table return an error

3. Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop daemon on
which the Hadoop framework will look for an available slot schedule a MapReduce operation.

A) NameNode
B) Secondary NameNode
C) DataNode
D) JobTracker
E) TaskTracker

4. Which describes how a client reads a file from HDFS?

A) The client queries the NameNode for the block location(s). The NameNode returns the block location(s)
to the client. The client reads the data directory off the DataNode(s).
B) The client contacts the NameNode for the block location(s). The NameNode contacts the DataNode
that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then
from the NameNode to the client.
C) The client contacts the NameNode for the block location(s). The NameNode then queries the
DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode redirects
the client to the DataNode that holds the requested data block(s). The client then reads the data directly
off the DataNode.
D) The client queries all DataNodes in parallel. The DataNode that contains the requested data responds
directly to the client. The client reads the data directly off the DataNode.

5. 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) There are no concerns with this approach. It is always advisable to use multiple reduces.
C) You will not be able to compress the intermediate data.
D) By using multiple reducers with the default HashPartitioner, output files may not be in globally sorted
order.

Solutions: