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Hortonworks HADOOP-PR000007 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Pig Development | 40% | - Data transformation, filtering, grouping, and joining - Integration with HCatalog and Hive - User-defined functions (UDFs) and optimization - Pig Latin syntax, data loading, and storage |
| Topic 2: Hadoop Fundamentals & HDFS | 20% | - Hadoop 2.0 architecture & YARN - HDFS file operations, permissions, and data management |
| Topic 3: Data Ingestion & Ecosystem Integration | 5% | - Workflow orchestration with Oozie - Sqoop and Flume usage basics |
| Topic 4: Apache Hive Development | 35% | - Hive architecture, metastore, and table management - HiveQL queries, joins, aggregations, and partitioning - Performance tuning and indexing - Data types, serialization, and file formats |
Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) Sample Questions:
Question #1
Which of the following tool was designed to import data from a relational database into HDFS?
A. Ambari
B. Flume
C. Sqoop
D. HCatalog
Question #2
You are developing a MapReduce job for sales reporting. The mapper will process input keys
representing the year (IntWritable) and input values representing product indentifies (Text).
Indentify what determines the data types used by the Mapper for a given job.
A. The data types specified in HADOOP_MAP_DATATYPES environment variable
B. The InputFormat used by the job determines the mapper's input key and value types.
C. The mapper-specification.xml file submitted with the job determine the mapper's input key and value
types.
D. The key and value types specified in the JobConf.setMapInputKeyClass and
JobConf.setMapInputValuesClass methods
Question #3
You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses
TextInputFormat: the mapper applies a regular expression over input values and emits key-values pairs
with the key consisting of the matching text, and the value containing the filename and byte offset.
Determine the difference between setting the number of reduces to one and settings the number of
reducers to zero.
A. With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one
reducer, all instances of matching patterns are gathered together in one file on HDFS.
B. With zero reducers, all instances of matching patterns are gathered together in one file on HDFS. With
one reducer, instances of matching patterns are stored in multiple files on HDFS.
C. There is no difference in output between the two settings.
D. With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances of
matching patterns are stored in a single file on HDFS.
Question #4
Which YARN component is responsible for monitoring the success or failure of a Container?
A. ApplicationMaster
B. NodeManager
C. ResourceManager
D. JobTracker
Question #5
Which one of the following statements regarding the components of YARN is FALSE?
A. The ResourceManager is responsible for scheduling and allocating resources
B. A Container executes a specific task as assigned by the ApplicationMaster
C. The ResourceManager monitors and restarts any failed Containers
D. A client application submits a YARW job to the ResourceManager
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: B | Question #3 Correct Answer: A | Question #4 Correct Answer: C | Question #5 Correct Answer: C |


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