Hortonworks : HADOOP-PR000007 Questions & Answers
Last Updated: Sep 17, 2026
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Hortonworks HADOOP-PR000007 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Hive Development | - Hive optimization and execution - Hive table definitions and queries |
| Topic 2: Hadoop Ecosystem Tools | - Workflow and processing frameworks - Sqoop and data ingestion |
| Topic 3: Pig Development | - Pig data types and UDFs - Pig Latin scripting |
| Topic 4: Hadoop Core Concepts | - HDFS architecture and commands - YARN and MapReduce fundamentals |
Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) Sample Questions:
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. Receives the same block reports as the active NameNode.
- C. Communicates directly with the active NameNode to maintain the state of the active NameNode.
- D. Runs on the same machine and shares the memory of the active NameNode.
Correct Answer: B 🗳️
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 sorted in ascending order.
- B. Keys are presented to a reducer in random order; values for a given key are not sorted.
- C. Keys are presented to reducer in sorted order; values for a given key are sorted in ascending order.
- D. Keys are presented to reducer in sorted order; values for a given key are not sorted.
Correct Answer: D 🗳️
When is the earliest point at which the reduce method of a given Reducer can be called?
- A. Not until all mappers have finished processing all records.
- B. It depends on the InputFormat used for the job.
- C. As soon as a mapper has emitted at least one record.
- D. As soon as at least one mapper has finished processing its input split.
Correct Answer: A 🗳️
You want to count the number of occurrences for each unique word in the supplied input data. You've
decided to implement this by having your mapper tokenize each word and emit a literal value 1, and then
have your reducer increment a counter for each literal 1 it receives. After successful implementing this, it
occurs to you that you could optimize this by specifying a combiner. Will you be able to reuse your
existing Reduces as your combiner in this case and why or why not?
- A. No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
- B. Yes, because the sum operation is both associative and commutative and the input and output types to
the reduce method match. - C. Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as
a combiner. - D. No, because the Combiner is incompatible with a mapper which doesn't use the same data type for
both the key and value. - E. No, because the Reducer and Combiner are separate interfaces.
Correct Answer: B 🗳️
Review the following data and Pig code.
M,38,95111 F,29,95060 F,45,95192 M,62,95102 F,56,95102
A = LOAD 'data' USING PigStorage('.') as (gender:Chararray, age:int,
zlp:chararray);
B = FOREACH A GENERATE age;
Which one of the following commands would save the results of B to a folder in hdfs named myoutput?
- A. DUMP B using PigStorage('myoutput');
- B. DUMP B INTO 'myoutput';
- C. STORE A INTO 'myoutput' USING PigStorage(',');
- D. STORE B INTO 'myoutput';
Correct Answer: D 🗳️
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