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Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) : HADOOP-PR000007
- Exam Code: HADOOP-PR000007
- Exam Name: Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer)
- Updated: Sep 12, 2026
- Q & A: 110 Questions and Answers
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Hortonworks HADOOP-PR000007 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion & Ecosystem Integration | 5% | - Sqoop and Flume usage basics - Workflow orchestration with Oozie |
| Topic 2: Hadoop Fundamentals & HDFS | 20% | - HDFS file operations, permissions, and data management - Hadoop 2.0 architecture & YARN |
| Topic 3: Apache Pig Development | 40% | - User-defined functions (UDFs) and optimization - Integration with HCatalog and Hive - Data transformation, filtering, grouping, and joining - Pig Latin syntax, data loading, and storage |
| Topic 4: Apache Hive Development | 35% | - Hive architecture, metastore, and table management - Performance tuning and indexing - HiveQL queries, joins, aggregations, and partitioning - Data types, serialization, and file formats |
Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) Sample Questions:
Question #1
In Hadoop 2.0, which TWO of the following processes work together to provide automatic failover of the
NameNode? Choose 2 answers
- A. QuorumManager
- B. ZKFailoverController
- C. ZooKeeper
- D. JournalNode
Correct Answer: B,D 🗳️
Question #2
MapReduce v2 (MRv2/YARN) is designed to address which two issues?
- A. Single point of failure in the NameNode.
- B. Ability to run frameworks other than MapReduce, such as MPI.
- C. Standardize on a single MapReduce API.
- D. Resource pressure on the JobTracker.
- E. Reduce complexity of the MapReduce APIs.
- F. HDFS latency.
Correct Answer: A,D 🗳️
Question #3
Which HDFS command uploads a local file X into an existing HDFS directory Y?
- A. hadoop scp X Y
- B. hadoop fs -get X Y
- C. hadoop fs-put X Y
- D. hadoop fs -localPut X Y
Correct Answer: C 🗳️
Question #4
The Hadoop framework provides a mechanism for coping with machine issues such as faulty
configuration or impending hardware failure. MapReduce detects that one or a number of machines are
performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and
the task finish first are used. This is called:
- A. Default Partitioner
- B. IdentityMapper
- C. IdentityReducer
- D. Combine
- E. Speculative Execution
Correct Answer: E 🗳️
Question #5
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 MapRunner that iterates over all key-value pairs in the entire file.
- C. Increase the parameter that controls minimum split size in the job configuration.
- D. Write a custom FileInputFormat and override the method isSplitable to always return false.
Correct Answer: D 🗳️
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