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

1. You have just executed a MapReduce job. Where is intermediate data written to after being emitted from
the Mapper's map method?

A) Intermediate data in streamed across the network from Mapper to the Reduce and is never written to
disk.
B) Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into
HDFS.
C) Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node
running the Reducer
D) Into in-memory buffers that spill over to the local file system of the TaskTracker node running the
Mapper.
E) Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into
HDFS.

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

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

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

4. What does the following WebHDFS command do?
Curl -1 -L "http://host:port/webhdfs/v1/foo/bar?op=OPEN"

A) Read a file /foo/bar
B) Make a directory /foo/bar
C) Delete a directory /foo/bar
D) List a directory /foo

5. You want to count the number of occurrences for each unique word in the supplied input data. You've
decided to implement this by having your mapper tokenize each word and emit a literal value 1, and then
have your reducer increment a counter for each literal 1 it receives. After successful implementing this, it
occurs to you that you could optimize this by specifying a combiner. Will you be able to reuse your
existing Reduces as your combiner in this case and why or why not?

A) No, because the Combiner is incompatible with a mapper which doesn't use the same data type for
both the key and value.
B) Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as
a combiner.
C) No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
D) No, because the Reducer and Combiner are separate interfaces.
E) Yes, because the sum operation is both associative and commutative and the input and output types to
the reduce method match.

Solutions: