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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:

SectionObjectives
Topic 1: Apache Pig- Pig execution model
  • 1. MapReduce translation of Pig scripts
    - Pig Latin Programming
    • 1. Data loading and transformation
      • 2. Filtering, grouping, and joining data
        Topic 2: Data Processing and Integration- Data ingestion and ETL workflows
        • 1. Batch processing concepts
          - Hadoop data formats
          • 1. Text, SequenceFile, and columnar formats
            Topic 3: Apache Hive- HiveQL Query Language
            • 1. SELECT, JOIN, and aggregation queries
              • 2. Schema definition and tables
                - Hive architecture
                • 1. Metastore and query execution engine
                  Topic 4: Hadoop Ecosystem Fundamentals- YARN and Resource Management
                  • 1. Cluster resource allocation
                    • 2. Resource scheduling concepts
                      - HDFS Architecture and Data Storage Concepts
                      • 1. Data replication and fault tolerance
                        • 2. NameNode and DataNode roles

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

                          1. Which one of the following statements describes a Pig bag. tuple, and map, respectively?

                          A) Ordered collection of maps, ordered collection of bags, and unordered set of key/value pairs
                          B) Ordered set of fields, ordered collection of tuples, ordered collection of maps
                          C) Unordered collection of maps, ordered collection of tuples, ordered set of key/value pairs
                          D) Unordered collection of tuples, ordered set of fields, set of key value pairs


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

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


                          3. Which of the following tool was designed to import data from a relational database into HDFS?

                          A) Ambari
                          B) Sqoop
                          C) HCatalog
                          D) Flume


                          4. Which one of the following Hive commands uses an HCatalog table named x?

                          A) SELECT x.-FROM org.apache.hcatalog.hive.HCatLoader('x');
                          B) SELECT * FROM org.apache.hcatalog.hive.HCatLoader('x');
                          C) Hive commands cannot reference an HCatalog table
                          D) SELECT * FROM x;


                          5. A combiner reduces:

                          A) The number of input files a mapper must process.
                          B) The amount of intermediate data that must be transferred between the mapper and reducer.
                          C) The number of values across different keys in the iterator supplied to a single reduce method call.
                          D) The number of output files a reducer must produce.


                          Solutions:

                          Question # 1
                          Answer: D
                          Question # 2
                          Answer: B
                          Question # 3
                          Answer: B
                          Question # 4
                          Answer: B
                          Question # 5
                          Answer: B

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