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

1. Which best describes what the map method accepts and emits?

A) It accepts a single key-value pairs as input and can emit only one key-value pair as output.
B) It accepts a single key-value pairs as input and can emit any number of key-value pair as output,
including zero.
C) It accepts a list key-value pairs as input and can emit only one key-value pair as output.
D) It accepts a single key-value pair as input and emits a single key and list of corresponding values as
output.

2. Identify the MapReduce v2 (MRv2 / YARN) daemon responsible for launching application containers and
monitoring application resource usage?

A) TaskTracker
B) ApplicationMasterService
C) NodeManager
D) ResourceManager
E) JobTracker
F) ApplicationMaster

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

A) 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.
B) 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.
C) 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.
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).

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

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

Solutions: