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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:

SectionObjectives
Data Processing and Integration- Data ingestion and ETL workflows
  • 1. Batch processing concepts
    - Hadoop data formats
    • 1. Text, SequenceFile, and columnar formats
      Apache Pig- Pig Latin Programming
      • 1. Filtering, grouping, and joining data
        • 2. Data loading and transformation
          - Pig execution model
          • 1. MapReduce translation of Pig scripts
            Hadoop Ecosystem Fundamentals- HDFS Architecture and Data Storage Concepts
            • 1. NameNode and DataNode roles
              • 2. Data replication and fault tolerance
                - YARN and Resource Management
                • 1. Resource scheduling concepts
                  • 2. Cluster resource allocation
                    Apache Hive- HiveQL Query Language
                    • 1. SELECT, JOIN, and aggregation queries
                      • 2. Schema definition and tables
                        - Hive architecture
                        • 1. Metastore and query execution engine

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

                          Question #1

                          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 DataCache and read the data into memory in the configure method of the mapper.
                          • B. Place the data file in the DistributedCache and read the data into memory in the map method of the mapper.
                          • C. Place the data file in the DistributedCache and read the data into memory in the configure 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.
                          Reveal Solution  Discussion  0

                          Correct Answer: A  🗳️

                          Question #2

                          In a large MapReduce job with m mappers and n reducers, how many distinct copy
                          operations will there be in the sort/shuffle phase?

                          • A. mXn (i.e., m multiplied by n)
                          • B. n
                          • C. mn (i.e., m to the power of n)
                          • D. m
                          • E. m+n (i.e., m plus n)
                          Reveal Solution  Discussion  0

                          Correct Answer: A  🗳️

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                          Question #3

                          You want to perform analysis on a large collection of images. You want to store this data in HDFS and process it with MapReduce but you also want to give your data analysts and data scientists the ability to process the data directly from HDFS with an interpreted high-level programming language like Python. Which format should you use to store this data in HDFS?

                          • A. CSV
                          • B. HTML
                          • C. Avro
                          • D. XML
                          • E. SequenceFiles
                          • F. JSON
                          Reveal Solution  Discussion  0

                          Correct Answer: C  🗳️

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                          Question #4

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

                          • A. As soon as a mapper has emitted at least one record.
                          • B. Not until all mappers have finished processing all records.
                          • C. It depends on the InputFormat used for the job.
                          • D. As soon as at least one mapper has finished processing its input split.
                          Reveal Solution  Discussion  0

                          Correct Answer: B  🗳️

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                          Question #5

                          Assuming default settings, which best describes the order of data provided to a reducer's reduce method:

                          • A. Both the keys and values passed to a reducer always appear in sorted order.
                          • B. Neither keys nor values are in any predictable order.
                          • C. The keys given to a reducer are in sorted order but the values associated with each key are in no predictable order
                          • D. The keys given to a reducer aren't in a predictable order, but the values associated with those keys always are.
                          Reveal Solution  Discussion  0

                          Correct Answer: C  🗳️

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