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

1. You need to perform statistical analysis in your MapReduce job and would like to call methods in the Apache Commons Math library, which is distributed as a 1.3 megabyte Java archive (JAR) file. Which is the best way to make this library available to your MapReducer job at runtime?

A) Have your system administrator copy the JAR to all nodes in the cluster and set its location in the HADOOP_CLASSPATH environment variable before you submit your job.
B) Package your code and the Apache Commands Math library into a zip file named JobJar.zip
C) Have your system administrator place the JAR file on a Web server accessible to all cluster nodes and then set the HTTP_JAR_URL environment variable to its location.
D) When submitting the job on the command line, specify the -libjars option followed by the JAR file path.

2. What is the term for the process of moving map outputs to the reducers?

A) Combining
B) Reducing
C) Partitioning
D) Shuffling and sorting

3. You need to create a job that does frequency analysis on input data. You will do this by writing a Mapper that uses TextInputFormat and splits each value (a line of text from an input file) into individual characters. For each one of these characters, you will emit the character as a key and an InputWritable as the value. As this will produce proportionally more intermediate data than input data, which two resources should you expect to be bottlenecks?

A) Processor and network I/O
B) Processor and RAM
C) Processor and disk I/O
D) Disk I/O and network I/O

4. 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. Hash values between specific ranges are associated with different buckets, and each bucket is assigned to a specific reducer.
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 and divides that valule modulo the number of reducers. The result determines the reducer assigned to process the key-value pair.
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.

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

Solutions: