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CCD-410 - Cloudera Certified Developer for Apache Hadoop (CCDH) - Dump Information

Vendor : Cloudera
Exam Code : CCD-410
Exam Name : Cloudera Certified Developer for Apache Hadoop (CCDH)
Questions and Answers : 60 Q & A
Updated On : September 19, 2017
PDF Download Mirror : CCD-410 Dump
Get Full Version : Pass4sure CCD-410 Full Version


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CCD-410 Questions and Answers

CCD-410

Class Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>


QUESTION: 56

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


Explanation:

You can use your reducer code as a combiner if the operation performed is commutative and associative.


Reference:

24 Interview Questions & Answers for Hadoop MapReduce developers,What are combiners? When should I use a combiner in my MapReduce Job?


QUESTION: 57

You want to perform analysis on a large collection of images. You want to store this data in HDFS and process it with MapReduce but you also want to give your data analysts and data scientists the ability to process the data directly from HDFS with an interpreted high-level programming language like Python. Which format should you use to store this data in HDFS?


  1. SequenceFiles

  2. Avro

  3. JSON

  4. HTML

  5. XML

  6. CSV


Answer: A


Explanation:

Using Hadoop Sequence Files

So what should we do in order to deal with huge amount of images? Use hadoop sequence files! Those are map files that inherently can be read by map reduce applications – there is an input format especially for sequence files – and are splitable by map reduce, so we can have one huge file that will be the input of many map tasks. By using those sequence files we are letting hadoop use its advantages. It can split the work into chunks so the processing is parallel, but the chunks are big enough that the process stays efficient. Since the sequence file are map file the desired format will be that the key will be text and hold the HDFS filename and the value will be BytesWritable and will contain the image content of the file.


Reference:

Hadoop binary files processing introduced by image duplicates finder


QUESTION: 58

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.


Answer: A


Explanation:

Hosting on local VMs

As well as large-scale cloud infrastructures, there is another deployment pattern: local VMs on desktop systems or other development machines. This is a good tactic if your physical machines run windows and you need to bring up a Linux system running Hadoop, and/or you want to simulate the complexity of a small Hadoop cluster.

Have enough RAM for the VM to not swap.

Don't try and run more than one VM per physical host, it will only make things slower. use file: URLs to access persistent input and output data.

consider making the default filesystem a file: URL so that all storage is really on the physical host. It's often faster and preserves data better.


QUESTION: 59

Your cluster’s HDFS block size in 64MB. You have directory containing 100 plain text files, each of which is 100MB in size. The InputFormat for your job is TextInputFormat. Determine how many Mappers will run?


  1. 64

  2. 100

  3. 200

  4. 640


Answer: C


Explanation:

Each file would be split into two as the block size (64 MB) is less than the file size (100 MB), so 200 mappers would be running.

Note:

If you're not compressing the files then hadoop will process your large files (say 10G), with a number of mappers related to the block size of the file.

Say your block size is 64M, then you will have ~160 mappers processing this 10G file (160*64 ~=

10G). Depending on how CPU intensive your mapper logic is, this might be an acceptable blocks size, but if you find that your mappers are executing in sub minute times, then you might want to increase the work done by each mapper (by increasing the block size to 128, 256, 512m - the actual size depends on how you intend to process the data).


Reference:

stackoverflow.com/questions/11014493/hadoop-mapreduce-appropriate-input- files-size(first answer, second paragraph)


QUESTION: 60

What is a SequenceFile?


  1. A SequenceFile contains a binary encoding of an arbitrary number of homogeneous writable objects.

  2. A SequenceFile contains a binary encoding of an arbitrary number of heterogeneous writable objects.

  3. A SequenceFile contains a binary encoding of an arbitrary number of WritableComparable objects, in sorted order.

  4. A SequenceFile contains a binary encoding of an arbitrary number key-value pairs. Each key must be the same type. Each value must be same type.


Answer: D

Explanation:

SequenceFile is a flat file consisting of binary key/value pairs. There are 3 different SequenceFile formats:

Uncompressed key/value records.

Record compressed key/value records - only 'values' are compressed here.

Block compressed key/value records - both keys and values are collected in 'blocks' separately and compressed. The size of the 'block' is configurable.


Reference:

wiki.apache.org/hadoop/SequenceFile


Cloudera CCD-410 Exam (Cloudera Certified Developer for Apache Hadoop (CCDH)) Detailed Information

Cloudera Certified Administrator for Apache Hadoop (CCAH)
Training Certification
| Hadoop Admin CCAH
A Cloudera Certified Administrator for Apache Hadoop (CCAH) certification proves that you have demonstrated your technical knowledge, skills, and ability to configure, deploy, maintain, and secure an Apache Hadoop cluster.
Cloudera Certified Administrator for Apache Hadoop (CCA-500)
Number of Questions: 60 questions
Time Limit: 90 minutes
Passing Score: 70%
Language: English, Japanese
Price: USD $295
REGISTER FOR CCA-500
Exam Sections and Blueprint
1. HDFS (17%)
Describe the function of HDFS daemons
Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing
Identify current features of computing systems that motivate a system like Apache Hadoop
Classify major goals of HDFS Design
Given a scenario, identify appropriate use case for HDFS Federation
Identify components and daemon of an HDFS HA-Quorum cluster
Analyze the role of HDFS security (Kerberos)
Determine the best data serialization choice for a given scenario
Describe file read and write paths
Identify the commands to manipulate files in the Hadoop File System Shell
2. YARN (17%)
Understand how to deploy core ecosystem components, including Spark, Impala, and Hive
Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons
Understand basic design strategy for YARN and Hadoop
Determine how YARN handles resource allocations
Identify the workflow of job running on YARN
Determine which files you must change and how in order to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN
3. Hadoop Cluster Planning (16%)
Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster
Analyze the choices in selecting an OS
Understand kernel tuning and disk swapping
Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA
Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
4. Hadoop Cluster Installation and Administration (25%)
Given a scenario, identify how the cluster will handle disk and machine failures
Analyze a logging configuration and logging configuration file format
Understand the basics of Hadoop metrics and cluster health monitoring
Identify the function and purpose of available tools for cluster monitoring
Be able to install all the ecoystme components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Cloudera Manager, Sqoop, Hive, and Pig
Identify the function and purpose of available tools for managing the Apache Hadoop file system
5. Resource Management (10%)
Understand the overall design goals of each of Hadoop schedulers
Given a scenario, determine how the FIFO Scheduler allocates cluster resources
Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN
Given a scenario, determine how the Capacity Scheduler allocates cluster resources
6. Monitoring and Logging (15%)
Understand the functions and features of Hadoop’s metric collection abilities
Analyze the NameNode and JobTracker Web UIs
Understand how to monitor cluster daemons
Identify and monitor CPU usage on master nodes
Describe how to monitor swap and memory allocation on all nodes
Identify how to view and manage Hadoop’s log files
Interpret a log file
Become a certified big data professional
Demonstrate your expertise with the most sought-after technical skills. Big data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the big data talent gap.
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Cloudera Certified Associate (CCA)
CCA exams test foundational skills and sets forth the groundwork for a candidate to achieve mastery under the CCP program
CCA Spark and Hadoop Developer
A CCA Spark and Hadoop Developer has proven his or her core developer skills to write and maintain Apache Spark and Apache Hadoop projects.
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Cloudera Certified Administrator for Apache Hadoop (CCAH)
Individuals who earn CCAH have demonstrated the core systems administrator skills sought by companies and organizations deploying Apache Hadoop.
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I passed, but I'd like to take the test again to improve my score. Can I do that?
Retakes are not allowed after the successful completion of a test. A test result found to be in violation of the retake policy will not be processed, which will result in no credit awarded for the test taken. Repeat violators will be banned from participation in the Cloudera Certification Program.
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If Cloudera determines, in its sole discretion, that a candidate has shared any content of an exam and is in violation of the Cloudera Certifications and Confidentiality Agreement, it reserves the right to take action up to and including, but not limited to, decertification of an individual and a permanent ban of the individual from Cloudera Certification programs, revocation of all previous Cloudera Certifications, notification to the candidate's employer, and notification to law enforcement agencies. Candidates found in violation of the Cloudera Certifications and Confidentiality Agreement forfeit all fees previously paid to Cloudera or to Cloudera's authorized vendors and may be required to pay additional fees for services rendered.
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Cloudera reserves the right to take action against any individual involved in fraudulent activities, including, but not limited to, fraudulent use of vouchers or promotional codes, reselling exam discounts and vouchers, cheating on an exam (including, but not limited to, creating, using, or distributing test dumps), alteration of score reports, alteration of completion certificates, violation of exam retake policies, or other activities deemed fraudulent by Cloudera.
If Cloudera determines, in its sole discretion, that fraudulent activity has taken place, it reserves the right to take action up to and including, but not limited to, decertification of an individual either temporarily until remediation occurs or as a permanent ban from Cloudera Certification programs, revocation of all previous Cloudera Certifications, notification to a candidate's employer, and notification to law enforcement agencies. Candidates found committing fraudulent activities forfeit all fees previously paid to Cloudera or to Cloudera's authorized vendors and may be required to pay additional fees for services rendered.
One form of government issued photo identification (i.e. driver's license, passport). Any international passport or government issued form of identification must contain Western (English) characters. You will be required to provide a means of photo identification before the exam can be launched. If acceptable proof of identification is not provided to the proctor prior to the exam, you will be refused entry to the exam. You must also consent to having your photo taken. The ID will be used for identity verification only and will not be stored. The proctor cannot release the exam to you until identification has been successfully verified and you have agreed to the terms and conditions of the exam. No refund or rescheduling is provided when an exam cannot be started due to failure to provide proper identification.
Benefits
Individuals
Performance-Based
Employers want to hire candidates with proven skills. The CCP program lets you demonstrate your skills in a rigorous hands-on environment.
Skills not Products
Cloudera’s ecosystem is defined by choice and so are our exams. CCP exams test your skills and give you the freedom to use any tool on the cluster. You are given a customer problem, a large data set, a cluster, and a time limit. You choose the tools, languages, and approach. (see below for cluster configuration)
Promote and Verify
As a CCP, you've proven you possess skills where it matters most. To help you promote your achievement, Cloudera provides the following for all current CCP credential holders:
A Unique profile link on certification.cloudera.com to promote your skills and achievements to your employer or potential employers which is also integrated to LinkedIn. (Example of a current CCP profile)
CCP logo for business cards, résumés, and online profiles
Current
The big data space is rapidly evolving. CCP exams are constantly updated to reflect the skills and tools relevant for today and beyond. And because change is the only constant in open-source environments, Cloudera requires all CCP credentials holders to stay current with three-year mandatory re-testing in order to maintain current CCP status and privileges.
Companies
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Cloudera’s hands-on exams require candidates to prove their skills on a live cluster, with real data, at scale. This means the CCP professional you hire or manage have skills where it matters.
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The CCP program provides a way to find, validate, and build a team of qualified technical professionals
Current
The big data space is rapidly evolving. CCP exams are constantly updated to reflect the skills and tools relevant for today and beyond. And because change is the only constant in open-source environments, Cloudera requires all CCP credentials holders to stay current with three-year mandatory re-testing.
CCP Data Engineer Exam (DE575) Details
Exam Question Format
You are given five to eight customer problems each with a unique, large data set, a CDH cluster, and four hours. For each problem, you must implement a technical solution with a high degree of precision that meets all the requirements. You may use any tool or combination of tools on the cluster (see list below) -- you get to pick the tool(s) that are right for the job. You must possess enough industry knowledge to analyze the problem and arrive at an optimal approach given the time allowed. You need to know what you should do and then do it on a live cluster under rigorous conditions, including a time limit and while being watched by a proctor.
Audience and Prerequisites
Candidates for CCP Data Engineer should have in-depth experience developing data engineering solutions and a high-level of mastery of the skills below. There are no other prerequisites.
Register for DE575
Required Skills
Data Ingest
The skills to transfer data between external systems and your cluster. This includes the following:
Import and export data between an external RDBMS and your cluster, including the ability to import specific subsets, change the delimiter and file format of imported data during ingest, and alter the data access pattern or privileges.
Ingest real-time and near-real time (NRT) streaming data into HDFS, including the ability to distribute to multiple data sources and convert data on ingest from one format to another.
Load data into and out of HDFS using the Hadoop File System (FS) commands.
Transform, Stage, Store
Convert a set of data values in a given format stored in HDFS into new data values and/or a new data format and write them into HDFS or Hive/HCatalog. This includes the following skills:
Convert data from one file format to another
Write your data with compression
Convert data from one set of values to another (e.g., Lat/Long to Postal Address using an external library)
Change the data format of values in a data set
Purge bad records from a data set, e.g., null values
Deduplication and merge data
Denormalize data from multiple disparate data sets
Evolve an Avro or Parquet schema
Partition an existing data set according to one or more partition keys
Tune data for optimal query performance
Data Analysis
Filter, sort, join, aggregate, and/or transform one or more data sets in a given format stored in HDFS to produce a specified result. All of these tasks may include reading from Parquet, Avro, JSON, delimited text, and natural language text. The queries will include complex data types (e.g., array, map, struct), the implementation of external libraries, partitioned data, compressed data, and require the use of metadata from Hive/HCatalog.
Write a query to aggregate multiple rows of data
Write a query to calculate aggregate statistics (e.g., average or sum)
Write a query to filter data
Write a query that produces ranked or sorted data
Write a query that joins multiple data sets
Read and/or create a Hive or an HCatalog table from existing data in HDFS
Workflow
The ability to create and execute various jobs and actions that move data towards greater value and use in a system. This includes the following skills:
Create and execute a linear workflow with actions that include Hadoop jobs, Hive jobs, Pig jobs, custom actions, etc.
Create and execute a branching workflow with actions that include Hadoop jobs, Hive jobs, Pig jobs, custom action, etc.
Orchestrate a workflow to execute regularly at predefined times, including workflows that have data dependencies
CCP Data Scientist (Cloudera Certified Professional Program)
CCP Data Scientists have demonstrated their skills in working with big data at an elite level. Candidates must prove their abilities on a live cluster with real data sets.
Prove your expertise at the highest level
Required Exams
DS700 – Descriptive and Inferential Statistics on Big Data
DS701 – Advanced Analytical Techniques on Big Data
DS702 - Machine Learning at Scale
CCA Spark and Hadoop Developer Exam (CCA175) Details
Number of Questions: 10–12 performance-based (hands-on) tasks on CDH5 cluster. See below for full cluster configuration
Time Limit: 120 minutes
Passing Score: 70%
Language: English, Japanese (forthcoming)
Price: USD $295
Exam Question Format
Each CCA question requires you to solve a particular scenario. In some cases, a tool such as Impala or Hive may be used. In other cases, coding is required. In order to speed up development time of Spark questions, a template is often provided that contains a skeleton of the solution, asking the candidate to fill in the missing lines with functional code. This template is written in either Scala or Python.
You are not required to use the template and may solve the scenario using a language you prefer. Be aware, however, that coding every problem from scratch may take more time than is allocated for the exam.
Evaluation, Score Reporting, and Certificate
Your exam is graded immediately upon submission and you are e-mailed a score report the same day as your exam. Your score report displays the problem number for each problem you attempted and a grade on that problem. If you fail a problem, the score report includes the criteria you failed (e.g., “Records contain incorrect data” or “Incorrect file format”). We do not report more information in order to protect the exam content. Read more about reviewing exam content on the FAQ.
If you pass the exam, you receive a second e-mail within a few days of your exam with your digital certificate as a PDF, your license number, a Linkedin profile update, and a link to download your CCA logos for use in your personal business collateral and social media profiles
Audience and Prerequisites
There are no prerequisites required to take any Cloudera certification exam. The CCA Spark and Hadoop Developer exam (CCA175) follows the same objectives as Cloudera Developer Training for Spark and Hadoop and the training course is an excellent preparation for the exam.
Register for CCA175
Required Skills
Data Ingest
The skills to transfer data between external systems and your cluster. This includes the following:
Import data from a MySQL database into HDFS using Sqoop
Export data to a MySQL database from HDFS using Sqoop
Change the delimiter and file format of data during import using Sqoop
Ingest real-time and near-real time (NRT) streaming data into HDFS using Flume
Load data into and out of HDFS using the Hadoop File System (FS) commands
Transform, Stage, Store
Convert a set of data values in a given format stored in HDFS into new data values and/or a new data format and write them into HDFS. This includes writing Spark applications in both Scala and Python (see note above on exam question format for more information on using either Scala or Python):
Load data from HDFS and store results back to HDFS using Spark
Join disparate datasets together using Spark
Calculate aggregate statistics (e.g., average or sum) using Spark
Filter data into a smaller dataset using Spark
Write a query that produces ranked or sorted data using Spark
Data Analysis
Use Data Definition Language (DDL) to create tables in the Hive metastore for use by Hive and Impala.
Read and/or create a table in the Hive metastore in a given schema
Extract an Avro schema from a set of datafiles using avro-tools
Create a table in the Hive metastore using the Avro file format and an external schema file
Improve query performance by creating partitioned tables in the Hive metastore
Evolve an Avro schema by changing JSON files

Cloudera CCD-410

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