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Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) : HADOOP-PR000007 Exam

HADOOP-PR000007 Exam Questions
  • Exam Code: HADOOP-PR000007
  • Exam Name: Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer)
  • Updated: Aug 03, 2026
  • Q & A: 110 Questions and Answers
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Hortonworks HADOOP-PR000007 Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Processing with Pig- Advanced Pig processing
  • 1. Joins and grouping
    • 2. User Defined Functions (UDFs)
      - Pig Latin fundamentals
      • 1. Loading and storing data
        • 2. Pig operators and transformations
          • 3. Data types and schemas
            Topic 2: Hadoop Ecosystem Integration- MapReduce basics
            - HDFS interaction
            - Data ingestion and ETL workflows
            Topic 3: Data Warehousing with Hive- HiveQL fundamentals
            • 1. SELECT queries and filtering
              • 2. Tables, partitions, and schemas
                - Advanced Hive features
                • 1. Metastore and HCatalog usage
                  • 2. Joins and aggregations

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

                    1. When is the earliest point at which the reduce method of a given Reducer can be called?

                    A) It depends on the InputFormat used for the job.
                    B) Not until all mappers have finished processing all records.
                    C) As soon as a mapper has emitted at least one record.
                    D) As soon as at least one mapper has finished processing its input split.


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


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

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


                    4. What is the disadvantage of using multiple reducers with the default HashPartitioner and distributing your
                    workload across you cluster?

                    A) By using multiple reducers with the default HashPartitioner, output files may not be in globally sorted
                    order.
                    B) You will longer be able to take advantage of a Combiner.
                    C) You will not be able to compress the intermediate data.
                    D) There are no concerns with this approach. It is always advisable to use multiple reduces.


                    5. Assuming the following Hive query executes successfully:

                    Which one of the following statements describes the result set?

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


                    Solutions:

                    Question # 1
                    Answer: B
                    Question # 2
                    Answer: A
                    Question # 3
                    Answer: C
                    Question # 4
                    Answer: A
                    Question # 5
                    Answer: C

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