Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer : Apache-Hadoop-Developer

  • Exam Code: Apache-Hadoop-Developer
  • Exam Name: Hadoop 2.0 Certification exam for Pig and Hive Developer
  • Updated: Aug 22, 2026
  • Q & A: 110 Questions and Answers

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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Ingestion25%- Import/export data using Sqoop
- Ingest streaming data with Flume
- Load data into HDFS from external sources
Topic 2: Hadoop Fundamentals & Architecture20%- YARN architecture and job execution
- MapReduce concepts and job lifecycle
- HDFS operations and file management
Topic 3: Apache Pig Development30%- Write and optimize Pig Latin scripts
- Debug and tune Pig jobs
- Data transformation, filtering, joining, and aggregation
Topic 4: Apache Hive Development25%- Use Hive functions, views, and metastore
- Write and optimize HiveQL queries
- Create and manage Hive tables, partitions, and buckets

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

1. In Hadoop 2.0, which one of the following statements is true about a standby NameNode?
The Standby NameNode:

A) Processes all client requests and block reports from the appropriate DataNodes.
B) Communicates directly with the active NameNode to maintain the state of the active NameNode.
C) Runs on the same machine and shares the memory of the active NameNode.
D) Receives the same block reports as the active NameNode.


2. Which Two of the following statements are true about hdfs? Choose 2 answers

A) HDFS works best when storing a large number of relatively small files
B) An HDFS file that is larger than dfs.block.size is split into blocks
C) Blocks are replicated to multiple datanodes
D) Block sizes for all files must be the same size


3. You've written a MapReduce job that will process 500 million input records and generated 500 million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reduces which is a potential bottleneck. A custom implementation of which interface is most likely to reduce the amount of intermediate data transferred across the network?

A) InputFormat
B) Partitioner
C) OutputFormat
D) Combiner
E) WritableComparable
F) Writable


4. In Hadoop 2.0, which TWO of the following processes work together to provide automatic failover of the NameNode? Choose 2 answers

A) ZooKeeper
B) ZKFailoverController
C) JournalNode
D) QuorumManager


5. Identify the tool best suited to import a portion of a relational database every day as files into HDFS, and generate Java classes to interact with that imported data?

A) Pig
B) Hue
C) fuse-dfs
D) Hive
E) Oozie
F) Flume
G) Sqoop


Solutions:

Question # 1
Answer: D
Question # 2
Answer: B,C
Question # 3
Answer: D
Question # 4
Answer: B,C
Question # 5
Answer: G

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