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

  • Exam Code: Apache-Hadoop-Developer
  • Exam Name: Hadoop 2.0 Certification exam for Pig and Hive Developer
  • Updated: Aug 26, 2026
  • Q&As: 110 Questions and Answers

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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:

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

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

1. 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) There is no difference in output between the two settings.
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) 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.
D) 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.


2. How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce?

A) Keys are presented to a reducer in random order; values for a given key are not sorted.
B) Keys are presented to a reducer in random order; values for a given key are sorted in ascending order.
C) Keys are presented to reducer in sorted order; values for a given key are not sorted.
D) Keys are presented to reducer in sorted order; values for a given key are sorted in ascending order.


3. Given the following Pig commands:

Which one of the following statements is true?

A) The $1 variable represents the second column of data in 'my.log'
B) The grouped relation is not valid
C) The $1 variable represents the first column of data in 'my.log'
D) The severe relation is not valid


4. You need to move a file titled "weblogs" into HDFS. When you try to copy the file, you can't. You know you have ample space on your DataNodes. Which action should you take to relieve this situation and store more files in HDFS?

A) Decrease the block size on all current files in HDFS.
B) Decrease the block size on your remaining files.
C) Increase the block size on all current files in HDFS.
D) Increase the block size on your remaining files.
E) Increase the number of disks (or size) for the NameNode.
F) Increase the amount of memory for the NameNode.


5. What does Pig provide to the overall Hadoop solution?

A) Database table and storage management services
B) Legacy language Integration with MapReduce framework
C) C++ interface to MapReduce and data warehouse infrastructure
D) Simple scripting language for writing MapReduce programs


Solutions:

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

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