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

1. You are developing a MapReduce job for sales reporting. The mapper will process input keys
representing the year (IntWritable) and input values representing product indentifies (Text).
Indentify what determines the data types used by the Mapper for a given job.

A) The data types specified in HADOOP_MAP_DATATYPES environment variable
B) The InputFormat used by the job determines the mapper's input key and value types.
C) The mapper-specification.xml file submitted with the job determine the mapper's input key and value
types.
D) The key and value types specified in the JobConf.setMapInputKeyClass and
JobConf.setMapInputValuesClass methods

2. Which of the following tool was designed to import data from a relational database into HDFS?

A) Sqoop
B) HCatalog
C) Flume
D) Ambari

3. Analyze each scenario below and indentify which best describes the behavior of the default partitioner?

A) The default partitioner computes the hash of the key and divides that valule modulo the number of
reducers. The result determines the reducer assigned to process the key-value pair.
B) The default partitioner implements a round-robin strategy, shuffling the key-value pairs to each reducer
in turn. This ensures an event partition of the key space.
C) The default partitioner computes the hash of the key. Hash values between specific ranges are
associated with different buckets, and each bucket is assigned to a specific reducer.
D) The default partitioner computes the hash of the value and takes the mod of that value with the number
of reducers. The result determines the reducer assigned to process the key-value pair.
E) The default partitioner assigns key-values pairs to reduces based on an internal random number
generator.

4. In a MapReduce job, you want each of your input files processed by a single map task. How do you
configure a MapReduce job so that a single map task processes each input file regardless of how many
blocks the input file occupies?

A) Write a custom FileInputFormat and override the method isSplitable to always return false.
B) Write a custom MapRunner that iterates over all key-value pairs in the entire file.
C) Increase the parameter that controls minimum split size in the job configuration.
D) Set the number of mappers equal to the number of input files you want to process.

5. 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) Combiner
C) Writable
D) OutputFormat
E) InputFormat
F) WritableComparable

Solutions:

Question # 1 Answer: B

Question # 2 Answer: A

Question # 3 Answer: A

Question # 4 Answer: A

Question # 5 Answer: B