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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:

Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:

Question 1

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. DataNode
B. Secondary NameNode
C. NameNode
D. TaskTracker
E. JobTracker

Question 2

Which one of the following classes would a Pig command use to store data in a table defined in HCatalog?

A. No special class is needed for a Pig script to store data in an HCatalog table
B. org.apache.hcatalog.pig.HCatOutputFormat
C. org.apache.hcatalog.pig.HCatStorer
D. Pig scripts cannot use an HCatalog table

Question 3

In the reducer, the MapReduce API provides you with an iterator over Writable values. What does calling the next () method return?

A. It returns a reference to a Writable object. The API leaves unspecified whether this is a reused object or a new object.
B. It returns a reference to the same Writable object if the next value is the same as the previous value, or a new Writable object otherwise.
C. It returns a reference to a different Writable object time.
D. It returns a reference to a Writable object from an object pool.
E. It returns a reference to the same Writable object each time, but populated with different data.

Question 4

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

Question 5

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

Solutions: