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HDPCD - Hortonworks Data Platform Certified Developer - Dump Information

Vendor : Hortonworks
Exam Code : HDPCD
Exam Name : Hortonworks Data Platform Certified Developer
Questions and Answers : 108 Q & A
Updated On : December 1, 2017
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HDPCD Questions and Answers


QUESTION: 97

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.

  1. There is no difference in output between the two settings.
  2. 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.
  3. 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.
  4. 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.

Answer: D


Explanation:

  • It is legal to set the number of reduce-tasks to zero if no reduction is desired.
    In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by setOutputPath(Path). The framework does not sort the map-outputs before writing them out to the FileSystem.
  • Often, you may want to process input data using a map function only. To do this,
    simply set mapreduce.job.reduces to zero. The MapReduce framework will not create any reducer tasks. Rather, the outputs of the mapper tasks will be the final output of the job.
    Note: Reduce
    In this phase the reduce(WritableComparable, Iterator, OutputCollector, Reporter) method is called for each <key, (list of values)> pair in the grouped inputs.
    The output of the reduce task is typically written to the FileSystem via OutputCollector.collect(WritableComparable, Writable).
    Applications can use the Reporter to report progress, set application-level status messages and update Counters, or just indicate that they are alive.
    The output of the Reducer is not sorted.

    QUESTION: 98

    Indentify the utility that allows you to create and run MapReduce jobs with any executable or script as the mapper and/or the reducer?

    1. Oozie
    2. Sqoop
    3. Flume
    4. Hadoop Streaming
    5. mapred

    Answer: D


    Explanation:

    Hadoop streaming is a utility that comes with the Hadoop distribution. The utility allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer.

    Reference:


    QUESTION: 99

    Which one of the following statements is true about a Hive-managed table?

    1. Records can only be added to the table using the Hive INSERT command.
    2. When the table is dropped, the underlying folder in HDFS is deleted.
    3. Hive dynamically defines the schema of the table based on the FROM clause of a SELECT query.
    4. Hive dynamically defines the schema of the table based on the format of the underlying data.

    Answer: B


    QUESTION: 100

    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?

    1. Increase the block size on all current files in HDFS.
    2. Increase the block size on your remaining files.
    3. Decrease the block size on your remaining files.
    4. Increase the amount of memory for the NameNode.
    5. Increase the number of disks (or size) for the NameNode.

    6. Decrease the block size on all current files in HDFS.

    Answer: C


    QUESTION: 101

    Which process describes the lifecycle of a Mapper?

    1. The JobTracker calls the TaskTracker’s configure () method, then its map () method and finally its close () method.
    2. The TaskTracker spawns a new Mapper to process all records in a single input split.
    3. The TaskTracker spawns a new Mapper to process each key-value pair.
    4. The JobTracker spawns a new Mapper to process all records in a single file.

    Answer: B


    Explanation:

    For each map instance that runs, the TaskTracker creates a new instance of your mapper.
    Note:
  • The Mapper is responsible for processing Key/Value pairs obtained from the InputFormat. The mapper may perform a number of Extraction and Transformation functions on the Key/Value pair before ultimately outputting none, one or many Key/Value pairs of the same, or different Key/Value type.
  • With the new Hadoop API, mappers extend the org.apache.hadoop.mapreduce.Mapper class. This class defines an 'Identity' map function by default - every input Key/Value pair obtained from the InputFormat is written out.
    Examining the run() method, we can see the lifecycle of the mapper:
    /**
  • Expert users can override this method for more complete control over the
  • execution of the Mapper.
  • @param context
  • @throws IOException
*/
public void run(Context context) throws IOException, InterruptedException { setup(context);
while (context.nextKeyValue()) {
map(context.getCurrentKey(), context.getCurrentValue(), context);
}
cleanup(context);

}

setup(Context) - Perform any setup for the mapper. The default implementation is a no-op method.
map(Key, Value, Context) - Perform a map operation in the given Key / Value pair. The default implementation calls Context.write(Key, Value)
cleanup(Context) - Perform any cleanup for the mapper. The default implementation
is a no-op method.

Reference:

Hadoop/MapReduce/Mapper

QUESTION: 102

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: 103

Which one of the following statements is FALSE regarding the communication between DataNodes and a federation of NameNodes in Hadoop 2.2?

  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: 104

In a MapReduce job with 500 map tasks, how many map task attempts will there be?

  1. It depends on the number of reduces in the job.
  2. Between 500 and 1000.
  3. At most 500.
  4. At least 500.
  5. Exactly 500.

Answer: D


Explanation:

From Cloudera Training Course:
Task attempt is a particular instance of an attempt to execute a task
  • There will be at least as many task attempts as there are tasks
  • If a task attempt fails, another will be started by the JobTracker
  • Speculative execution can also result in more task attempts than completed tasks

QUESTION: 105

Review the following &apos;data&apos; file and Pig code.

Which one of the following statements is true?

  1. The Output Of the DUMP D command IS (M,{(M,62.95102),(M,38,95111)})
  2. The output of the dump d command is (M, {(38,95in),(62,95i02)})
  3. The code executes successfully but there is not output because the D relation is empty
  4. The code does not execute successfully because D is not a valid relation

Answer: A


QUESTION: 106

Which one of the following is NOT a valid Oozie action?

  1. mapreduce
  2. pig
  3. hive
  4. mrunit

Answer: D


QUESTION: 107

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: 108

Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop daemon on which the Hadoop framework will look for an available slot schedule a MapReduce operation.

  1. TaskTracker
  2. NameNode
  3. DataNode
  4. JobTracker
  5. Secondary NameNode

Answer: D


Explanation:

JobTracker is the daemon service for submitting and tracking MapReduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. Each slave node is configured with job tracker node location. The JobTracker is single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted. JobTracker in Hadoop performs following actions(from Hadoop Wiki:)
Client applications submit jobs to the Job tracker.
The JobTracker talks to the NameNode to determine the location of the data
The JobTracker locates TaskTracker nodes with available slots at or near the data The JobTracker submits the work to the chosen TaskTracker nodes.
The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker.
A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable. When the work is completed, the JobTracker updates its status. Client applications can poll the JobTracker for information.

Reference:

24 Interview Questions & Answers for Hadoop MapReduce developers, What is
a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster?

Hortonworks HDPCD Exam (Hortonworks Data Platform Certified Developer) Detailed Information

THE HDPCD EXAM
Our certifications are exclusively hands-on, performance-based exams that require you to complete a set of tasks. By performing tasks on an actual Hadoop cluster instead of just guessing at multiple-choice questions, Hortonworks Certified Professionals have proven competency and Big Data expertise. The HDP Certified Developer (HDPCD) exam has three main categories of tasks that involve:
The exam is based on the Hortonworks Data Platform 2.4 installed and managed with Ambari 2.2, which includes Pig 0.15.0, Hive 1.2.1, Sqoop 1.4.6, and Flume 1.5.2. Each candidate will be given access to an HDP 2.4 cluster along with a list of tasks to be performed on that cluster.
To be fully prepared for the HDP Certified Developer exam, candidates should be able to complete all of the exam objectives.
TAKE THE EXAM ANYTIME, ANYWHERE
The HDP Certified Developer (HDPCD) exam is available from any computer, anywhere, at any time. All you need is a webcam and a good Internet connection. The cost of the exam is $250 USD per attempt.
HDP Certified Developer (HDPCD)
Hortonworks Certification Overview
At Hortonworks University, the mission of our certification
program is to create meaningful certifications that are recognized
in the industry as a confident measure of qualified, capable big
data experts. How do we accomplish that mission?
1. Our certifications are exclusively hands-on,
performance-based exams that require you to
complete a set of tasks.
2. By performing tasks on an actual Hadoop cluster
instead of just guessing at multiple-choice questions, an
HDP Certified Developer has proven competency and
big data expertise.
The HDP Certified Developer (HDPCD) exam is for Hadoop
developers working with frameworks like Pig, Hive, Sqoop and
Flume.
Purpose of the Exam
The purpose of this exam is to provide organizations that use
Hadoop with a means of identifying suitably qualified staff to
develop Hadoop applications for storing, processing, and
analyzing data stored in Hadoop using the open-source tools of
the Hortonworks Data Platform (HDP), including Pig, Hive, Sqoop
and Flume.
Exam Description
The exam has three main categories of tasks that involve:
• Data ingestion
• Data transformation
• Data analysis
The exam is based on the Hortonworks Data Platform 2.4
installed and managed with Ambari 2.2, which includes Pig
0.15.0, Hive 1.2.1, Sqoop 1.4.6, and Flume 1.5.2. Each candidate
will be given access to an HDP 2.4 cluster along with a list of
tasks to be performed on that cluster.
Exam Objectives
View the complete list of objectives on our website at
http://hortonworks.com/training/certification/hdpcd/.
Language
The exam is delivered in English.
Take the Exam Anytime, Anywhere
The$HDPCD$exam$is$available$from$any$computer,$anywhere,$at$
any$time.$All$you$need$is$a$webcam$and$a$good$Internet$
connection.
How to Register
Candidates need$to$create$an$account$at$www.examslocal.com.$
Once$you$are$registered$and$logged$in,$select$“Schedule$an$
Exam”,$and$then$enter$“Hortonworks”$in$the$“Search$Here”$field$
to$locate$and$select$the$HDP$Certified$Developer$exam. The$cost$
of$the$exam$is$$250$USD per$attempt.
Duration
2$hours
Description of the Minimally Qualified Candidate
The Minimally Qualified Candidate (MQC) for this certification can
develop Hadoop applications for ingesting, transforming, and
analyzing data stored in Hadoop using the open-source tools of
the Hortonworks Data Platform, including Pig, Hive, Sqoop and
Flume. Those certified are recognized as having high level of skill
in Hadoop application development and have demonstrated that
knowledge by performing the objectives of the HDPCD exam on
a live HDP cluster.
Prerequisites
Candidates for the HPDCD exam should be able to perform each
of the tasks in the list on our website at
http://hortonworks.com/training/certification/hdpcd/. Candidates
are also encouraged to attempt the practice exam.
Hortonworks University
Hortonworks University is your expert source for big data training
and certification. Public and private on-site courses are available
for developers, administrators, data analysts and other IT
professionals involved in implementing big data solutions.
Classes combine presentation material with industry-leading
hands-on labs that fully prepare students for real-world Hadoop
scenarios.

Hortonworks HDPCD

HDPCD exam :: Article by ArticleForgeHDPCD Certification
follow these steps to register for the HDPCD exam:
Create an account at www.examslocal.com.
when you are registered and logged in, select “schedule an examination”, after which enter “Hortonworks” within the “Search here” box.
locate and select the “Hortonworks : HDP certified Developer (HDPCD) – English” examination.
opt for the date and time that you need to effort the examination, and that is it!

HDP licensed Developer (HDPCD) exam – statistics Ingestion HDP licensed Developer this is first publish from HDPCD exam training series. during this publish, i'll talk about and remedy projects regarding facts ingestion a part of the examination.
licensed Badge indicates my badge from Hortonworks. Prerequisite This series is heavily influenced with the aid of Hortonworks practice exam. You may additionally observe the instruction to setup ambiance at apply exam. that you would be able to also be taught from this sequence if in case you have an existing Hadoop atmosphere. which you can also download Hortonworks sandbox and might installation it on your desktop for native setup. which you can locate guidance to download and set up at Hortoworks Sandbox.
in the event you choose to use Hortonworks sandbox or an existing atmosphere, I even have provide the dataset together with options to initiatives at HDPCD-Certification. ambiance organized via apply exam guidance have alread HDPCDy got dataset but you could should download the solutions in case you are looking to.
here is a really palms-on collection, so roll sleeves and get able to hold your hands soiled.
project 00 during this task, we are able to download the datasets for our subsequent initiatives. The datasets together with options can be downloaded from HDPCD Certification. which you can download the contents as a zipper file through clicking down load ZIP button once you are on HDPCD Certification page. once you down load the contents, unzip it in your domestic directory.
project 01 as the dataset is downloaded, during this project, we will add dataset into hadoop the use of hdfs commands. to see the details of this project, appear into /home/horton/HDPCD-Certification-master/data_Ingestion/Task01. The difficulty can be solved with the aid of working following instructions.
cd /domestic/horton/HDPCD-Certification-grasp hdfs dfs -put datasets/flightdelays task 02 in this task, we will be ingesting records the usage of Apache Flume. The task element is given in/domestic/horton/HDPCD-Certification-master/data_Ingestion/Task02. Flume requires a file which incorporates the ingestion circulation. I even have created a configuration file for this task at and we can should run flume agent as follows:
cd /home/horton/HDPCD-Certification-grasp/data_Ingestion/options flume-ng agent --identify a1 --conf-file Task02.conf this will birth a flume agent and it'll ingest information into hadoop. Don’t neglect to stop the agent by means of pressing Ctrl+c. In supply listing, all ingested info can be suffixed with the aid of .completed.
task 03 In Task03 we can be exporting statistics from hadoop to a relational database the usage of Apache Sqoop. You deserve to make sure you create a table in relational database before starting export. I even have been using atmosphere advised for observe examination and database setup exists in. First we can reproduction a file into hadoop which should be exported to relational database.
hdfs dfs -mkdir weather cd /home/horton/HDPCD-Certification-grasp hdfs dfs -put datasets/flightdelays/sfo_weather.csv Now because the information is ingested in hadoop, we're able to execute sqoop command. The command is provide in options directory as Task03.sh as
sqoop export --connect jdbc:mysql://namenode/flightinfo --username root --password hadoop --desk weather --export-dir /person/horton/climate --enter-fields-terminated-by way of ',' this can export records in climate table in flightinfo database. If method runs without any errors, you could view the exported facts in mysql database.
assignment 04 Now we can be performing the opposite of Task03. in this assignment we may be importing records from mysql desk to hadoop. As we are the usage of the same setup as Task03 has, no extra setup is required. The export command is given in solutions listing as Task04.sh as
sqoop import --join jdbc:mysql://namenode/flightinfo --username root --password hadoop --question 'opt for * FROM climate the place $conditions' --break up-through year --target-dir /consumer/horton/sqoopImport right here statistics is imported the usage of a query however that you may choose desk, column and the place command alternatives to imitate the command as smartly.
abstract in this session, we ingested information the usage of hdfs command, flume and sqoop utilities. Now we are able to stream to subsequent part of certfication the place ingested facts could be modified the usage of Pig.
assistance
  • Open one command terminal with three tabs
  • a tab to run instructions
  • a tab to edit answer information
  • a tab to run any hdfs command
  • examination atmosphere comes with net hyperlinks to documentation of Pig, Hive, Flume and Sqoop. You may additionally open all these links in one net browser window to retailer time.
  • connected author: Rashid Ali
    A application skilled who architects softwares to clear up issues. i like fixing puzzles, managing records and having first-rate time with household and friends. View all posts through Rashid Ali

    Hortonworks data Platform certified Developer (HDPCD) CertificationNo outcomes discovered, are trying new key phrase!I actually have effectively accomplished the HDPCD exam on October 2015. Hortonworks has redesigned its usual examination pattern. instead of multiple-option questions, the examination contains tasks that have to be accomplished on a live, three-node Hortonworks records Platform cluster.
    Crushing Certification assessments The HDP licensed Developer (HDPCD) examination is complicated, you deserve to be aware of Pig, Hive, Sqoop, and Flume.
    step one in preparing is to walk through all the steps outlined in the exam guide. basically examine the Apache documentation for every assignment, follow the tutorials at the Hortonworks web site and do the arms-on from GitHub. You should be in a position to do the palms-on work!!!!  The look at various is palms-on and not multiple choice. You deserve to recognize the instructions and get them operating. I imply setting up the Amazon cloud the usage of the exam instructions AND downloading a Sandbox so so you might try out all the Pig, Flume, Hive, and Sqoop commands and queries. You should run some from Ambari and a few from the command line. You should be comfortable and be aware of the syntax for both. The exam is strictly timed, and you would not have entry to Google.
    Now for the critical counsel:
  • Search the Hortonworks community, you'll locate solutions, tutorials, and advantageous guidelines on the entire above outlined Hadoop equipment. additionally that you would be able to submit questions there and they will quickly be answered with the aid of experienced Hadoop individuals and viable actual assignment committers.
  • study HDPCD the presentations from Hadoop Summit and Hortonworks. it be a great way to get history and spot some causes at the back of the tools. yet another Hadoop Summit is coming this month, so there can be more fabric quickly.
  • Watch training movies on Youtube. Hortonworks and others have a ton of different americans have put lots of of hours of training obtainable.
  • if you are a Hortonworks client, you may additionally have entry to the Self-Paced learning Library which incorporates online classes with videos, displays, and click on-via getting to know.
  • Come to a meetup, there are hundreds Hadoop meetups around that give palms-on and are living displays on quite a lot of Hadoop equipment. that you can also ask inquiries to certified developers, specialists, and other people learning.
  • study HDPCD DZone articles on Pig (Intro), Flume, Hive, and Sqoop.
  • analyze this excellent set of Hadoop Tutorials are CoreServlets.com
  • read HDPCD the documentation on the Apache websites: Pig, Flume, Sqoop, and Hive.
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    hortonworks,hadoop,certification,massive information,sqoop,flume,pig,hive

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