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

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

A) 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.
B) 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.
C) 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.
D) 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).

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

3. Which HDFS command displays the contents of the file x in the user's HDFS home directory?

A) hadoop fs -cp x
B) hadoop fs -Is x
C) hdfs fs -get x
D) hadoop fs -cat x

4. You have written a Mapper which invokes the following five calls to the OutputColletor.collect method:
output.collect (new Text ("Apple"), new Text ("Red") ) ;
output.collect (new Text ("Banana"), new Text ("Yellow") ) ;
output.collect (new Text ("Apple"), new Text ("Yellow") ) ;
output.collect (new Text ("Cherry"), new Text ("Red") ) ;
output.collect (new Text ("Apple"), new Text ("Green") ) ;
How many times will the Reducer's reduce method be invoked?

A) 1
B) 3
C) 6
D) 5
E) 0

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

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

Solutions: