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

1. 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.

2. Assuming the following Hive query executes successfully:

Which one of the following statements describes the result set?

A) A frequency distribution of the top 80 words that follow the subsequence "you are" in the lines column of the inputdata table.
B) A trigram of the top 80 sentences that contain "you are" followed by a null space in the lines column of the inputdata table.
C) An 80-value ngram of sentences that contain the words "you" or "are" in the lines column of the inputdata table.
D) A bigram of the top 80 sentences that contain the substring "you are" in the lines column of the input data A1 table.

3. A NameNode in Hadoop 2.0 manages ______________.

A) No namespaces
B) An arbitrary number of namespaces
C) Two namespaces: an active namespace and a backup namespace
D) A single namespace

4. How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce?

A) Keys are presented to reducer in sorted order; values for a given key are sorted in ascending order.
B) Keys are presented to a reducer in random order; values for a given key are not sorted.
C) Keys are presented to a reducer in random order; values for a given key are sorted in ascending order.
D) Keys are presented to reducer in sorted order; values for a given key are not sorted.

5. MapReduce v2 (MRv2/YARN) is designed to address which two issues?

A) Standardize on a single MapReduce API.
B) HDFS latency.
C) Ability to run frameworks other than MapReduce, such as MPI.
D) Reduce complexity of the MapReduce APIs.
E) Single point of failure in the NameNode.
F) Resource pressure on the JobTracker.

Solutions:

Question # 1 Answer: B

Question # 2 Answer: A

Question # 3 Answer: D

Question # 4 Answer: D

Question # 5 Answer: E,F