Learn with Hortonworks : Apache-Hadoop-Developer training material for 100% pass
Updated: Aug 26, 2026
No. of Questions: 110 Questions & Answers with Testing Engine
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Hortonworks Apache-Hadoop-Developer Exam Overview:
| Certification Vendor: | Hortonworks |
|---|---|
| Exam Name: | Hortonworks Certified Apache Hadoop 2.0 Developer (Pig and Hive Developer) |
| Exam Number: | HADOOP-PR000007 / HDPCD / Apache-Hadoop-Developer |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 8–12 performance-based tasks / approx. 110 multiple-choice |
| Exam Format: | Scenario-based tasks, Multiple-choice questions, Performance-based hands-on tasks |
| Related Certifications: | Hortonworks Certified Associate (HCA) HDP Certified Administrator (HDPCA) |
| Exam Duration: | 120 minutes |
| Passing Score: | 70% |
| Exam Price: | $250 USD |
| Recommended Training: | Hortonworks Official Developer Training Apache Pig & Hive Official Documentation |
| Exam Registration: | Hortonworks Certification Portal |
| Sample Questions: | Hortonworks Apache-Hadoop-Developer Sample Questions |
| Exam Way: | Online proctored exam on a remote HDP 2.2 / 2.4 cluster |
| Pre Condition: | Recommended: 6+ months hands-on experience with HDP, Pig, Hive, Sqoop, Flume; no mandatory prerequisites |
| Official Syllabus URL: | https://hortonworks.com/services/training-certification/ (program retired) |
Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Pig Development | 30% | - Debug and tune Pig jobs - Data transformation, filtering, joining, and aggregation - Write and optimize Pig Latin scripts |
| Topic 2: Apache Hive Development | 25% | - Create and manage Hive tables, partitions, and buckets - Use Hive functions, views, and metastore - Write and optimize HiveQL queries |
| Topic 3: Hadoop Fundamentals & Architecture | 20% | - HDFS operations and file management - YARN architecture and job execution - MapReduce concepts and job lifecycle |
| Topic 4: Data Ingestion | 25% | - Import/export data using Sqoop - Load data into HDFS from external sources - Ingest streaming data with Flume |
Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:
Question 1
You need to run the same job many times with minor variations. Rather than hardcoding all job configuration options in your drive code, you've decided to have your Driver subclass org.apache.hadoop.conf.Configured and implement the org.apache.hadoop.util.Tool interface.
Indentify which invocation correctly passes.mapred.job.name with a value of Example to Hadoop?
A. hadoop setproperty ("mapred.job.name=Example") MyDriver input output
B. hadoop "mapred.job.name=Example" MyDriver input output
C. hadoop setproperty mapred.job.name=Example MyDriver input output
D. hadoop MyDrive -D mapred.job.name=Example input output
E. hadoop MyDriver mapred.job.name=Example input output
Question 2
Which one of the following statements describes the relationship between the NodeManager and the ApplicationMaster?
A. The NodeManager requests resources from the ApplicationMaster
B. The ApplicationMaster starts the NodeManager outside of a Container
C. The NodeManager creates an instance of the ApplicationMaster
D. The ApplicationMaster starts the NodeManager in a Container
Question 3
MapReduce v2 (MRv2/YARN) splits which major functions of the JobTracker into separate
daemons? Select two.
A. Launching tasks
B. Job coordination between the ResourceManager and NodeManager
C. Job scheduling/monitoring
D. Resource management
E. Managing file system metadata
F. Managing tasks
G. Heath states checks (heartbeats)
H. MapReduce metric reporting
Question 4
When is the earliest point at which the reduce method of a given Reducer can be called?
A. As soon as at least one mapper has finished processing its input split.
B. As soon as a mapper has emitted at least one record.
C. Not until all mappers have finished processing all records.
D. It depends on the InputFormat used for the job.
Question 5
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. OutputFormat
C. Combiner
D. WritableComparable
E. Writable
F. Partitioner
Solutions:
| Question 1 Answer: D | Question 2 Answer: C | Question 3 Answer: C,D | Question 4 Answer: C | Question 5 Answer: C |
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