Latest PDF of Hadoop-PR000007: Hortonworks Certified Apache Hadoop 2.0 Developer (Pig and Hive Developer)

Hortonworks Certified Apache Hadoop 2.0 Developer (Pig and Hive Developer) Practice Test

Hadoop-PR000007 exam Format | Course Contents | Course Outline | exam Syllabus | exam Objectives

Exam Details for Hadoop-PR000007 Hortonworks Certified Apache Hadoop 2.0 Developer (Pig and Hive Developer):

Number of Questions: The Hadoop-PR000007 exam typically consists of 50-60 multiple-choice questions.

Time Limit: The exam has a time limit of 2 hours (120 minutes).

Course Outline:
The Hadoop-PR000007 certification exam focuses on assessing the knowledge and skills required to develop and work with Apache Hadoop 2.0, specifically focusing on Pig and Hive for data processing and analytics. The course outline covers the following key topics:

1. Introduction to Apache Hadoop:
- Overview of Hadoop ecosystem and its components
- Hadoop architecture and distributed file system (HDFS)
- MapReduce and YARN frameworks
- Hadoop cluster setup and configuration

2. Apache Pig:
- Introduction to Pig and Pig Latin scripting language
- Loading and storing data using Pig
- Transforming and manipulating data using Pig Latin operations
- User-defined functions (UDFs) in Pig
- Pig optimization techniques and performance tuning

3. Apache Hive:
- Introduction to Hive and HiveQL query language
- Creating and managing tables in Hive
- Data manipulation and querying with HiveQL
- Partitioning and bucketing in Hive
- Hive optimization techniques and performance tuning

4. Data Processing with Pig and Hive:
- Integrating Pig and Hive with Hadoop ecosystem components
- Using Pig and Hive for data transformations and analytics
- Joining and aggregating data in Pig and Hive
- Handling complex data types and nested structures
- Debugging and troubleshooting Pig and Hive scripts

Exam Objectives:
The Hadoop-PR000007 exam aims to assess the following objectives:

1. Understanding of Apache Hadoop architecture and its key components.
2. Proficiency in using Pig for data loading, transformation, and analysis.
3. Knowledge of Hive and its capabilities for data querying and management.
4. Competence in integrating Pig and Hive with other Hadoop ecosystem components.
5. Familiarity with performance optimization and debugging techniques in Pig and Hive.

Exam Syllabus:
The Hadoop-PR000007 exam covers the following syllabus:

1. Introduction to Apache Hadoop
- Overview of Hadoop ecosystem and its components
- Hadoop architecture and distributed file system (HDFS)
- MapReduce and YARN frameworks
- Hadoop cluster setup and configuration

2. Apache Pig
- Introduction to Pig and Pig Latin scripting language
- Loading and storing data using Pig
- Transforming and manipulating data using Pig Latin operations
- User-defined functions (UDFs) in Pig
- Pig optimization techniques and performance tuning

3. Apache Hive
- Introduction to Hive and HiveQL query language
- Creating and managing tables in Hive
- Data manipulation and querying with HiveQL
- Partitioning and bucketing in Hive
- Hive optimization techniques and performance tuning

4. Data Processing with Pig and Hive
- Integrating Pig and Hive with Hadoop ecosystem components
- Using Pig and Hive for data transformations and analytics
- Joining and aggregating data in Pig and Hive
- Handling complex data types and nested structures
- Debugging and troubleshooting Pig and Hive scripts

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Hadoop-PR000007
Hortonworks Certified Apache Hadoop 2.0 Developer (Pig and Hive Developer)
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Which one of the following statements is FALSE regarding the communication between DataNodes and a federation of NameNodes in Hadoop 2.0?
1. Each DataNode receives commands from one designated master NameNode.
2. DataNodes send periodic heartbeats to all the NameNodes.
3. Each DataNode registers with all the NameNodes.
4. DataNodes send periodic block reports to all the NameNodes.
Answer: A
QUESTION: 100
A client application creates an HDFS file named foo.txt with a replication factor of 3. Identify which best describes the file access rules in HDFS if the file has a single block that is stored on data nodes A, B and C?
1. The file will be marked as corrupted if data node B fails during the creation of the file.
2. Each data node locks the local file to prohibit concurrent readers and writers of the file.
3. Each data node stores a copy of the file in the local file system with the same name as the HDFS file.
4. The file can be accessed if at least one of the data nodes storing the file is available.
Answer: D
QUESTION: 101
Examine the following Hive statements:
Assuming the statements above execute successfully, which one of the following statements is true?
1. Each reducer generates a file sorted by age
2. The SORT BY command causes only one reducer to be used
3. The output of each reducer is only the age column
4. The output is guaranteed to be a single file with all the data sorted by age
Answer: A
QUESTION: 102
When can a reduce class also serve as a combiner without affecting the output of a MapReduce program?
1. When the types of the reduce operation�s input key and input value match the types of the reducer�s output key and output value and when the reduce operation is both communicative and associative.
2. When the signature of the reduce method matches the signature of the combine method.
3. Always. Code can be reused in Java since it is a polymorphic object-oriented programming language.
4. Always. The point of a combiner is to serve as a mini-reducer directly after the map phase to increase performance.
5. Never. Combiners and reducers must be implemented separately because they serve different purposes.
Answer: A
QUESTION: 103
You want to run Hadoop jobs on your development workstation for testing before you submit them to your production cluster. Which mode of operation in Hadoop allows you to most closely simulate a production cluster while using a single machine?
1. Run all the nodes in your production cluster as virtual machines on your development workstation.
2. Run the hadoop command with the �jt local and the �fs file:///options.
3. Run the DataNode, TaskTracker, NameNode and JobTracker daemons on a single machine.
4. Run simldooop, the Apache open-source software for simulating Hadoop clusters.
Which one of the following files is required in every Oozie Workflow application?
1. job.properties
2. Config-default.xml
3. Workflow.xml
4. Oozie.xml
Answer: C
QUESTION: 105
Workflows expressed in Oozie can contain:
1. Sequences of MapReduce and Pig. These sequences can be combined with other actions including forks, decision points, and path joins.
2. Sequences of MapReduce job only; on Pig on Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins.
3. Sequences of MapReduce and Pig jobs. These are limited to linear sequences of actions with exception handlers but no forks.
4. Iterntive repetition of MapReduce jobs until a desired answer or state is reached.
Answer: A
QUESTION: 106
Which best describes what the map method accepts and emits?
1. It accepts a single key-value pair as input and emits a single key and list of corresponding values as output.
2. It accepts a single key-value pairs as input and can emit only one key-value pair as output.
3. It accepts a list key-value pairs as input and can emit only one key-value pair as output.
4. It accepts a single key-value pairs as input and can emit any number of key-value pair as output, including zero.
Which HDFS command copies an HDFS file named foo to the local filesystem as localFoo?
1. hadoop fs -get foo LocalFoo
2. hadoop -cp foo LocalFoo
3. hadoop fs -Is foo
4. hadoop fs -put foo LocalFoo
Answer: A
QUESTION: 108
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?
1. Oozie
2. Flume
3. Pig
4. Hue
5. Hive
6. Sqoop
7. fuse-dfs
Answer: F

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Hortonworks Certified Apache Hadoop 2.0 Developer (Pig and Hive Developer) Practice Test

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