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CCD-470 - Cloudera Certified Developer for Apache Hadoop CDH4 Upgrade (CCDH) - Dump Information
Vendor | : | Cloudera |
Exam Code | : | CCD-470 |
Exam Name | : | Cloudera Certified Developer for Apache Hadoop CDH4 Upgrade (CCDH) |
Questions and Answers | : | 94 Q & A |
Updated On | : | November 24, 2017 |
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Cloudera CCD-470 Dumps and Practice Tests with Real Questions
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- Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run.
- Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.
- Because combiners perform local aggregation of word counts, thereby reducing the number of key-value pairs that need to be snuff let across the network to the reducers.
Answer: A
Explanation:
- Simply speaking a combiner can be considered as a “mini reducer” that will be applied potentially several times still during the map phase before to send the new (hopefully reduced) set of key/value pairs to the reducer(s). This is why a combiner must implement the Reducer interface (or extend the Reducer class as of hadoop 0.20).
- Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be transferred across to the reducers. You can use your reducer code as a combiner if the operation performed is commutative and associative. The execution of combiner is not guaranteed, Hadoop may or may not execute a combiner. Also, if required it may execute it more then 1 times. Therefore your MapReduce jobs should not depend on the combiners execution.
QUESTION: 90
Which two of the following are valid statements? (Choose two)- HDFS is optimized for storing a large number of files smaller than the HDFS block size.
- HDFS has the Characteristic of supporting a "write once, read many" data access model.
- HDFS is a distributed file system that replaces ext3 or ext4 on Linux nodes in a Hadoopcluster.
- HDFS is a distributed file system that runs on top of native OS filesystems and is well suited to storage of very large data sets.
Answer: B, D
Explanation:
B: HDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to besatisfied at streaming speeds. HDFS supports write-once-read-many semantics on files. D: - Hadoop Distributed File System: A distributed file system that provides high- throughput access to application data.
- DFS is designed to support very large files.
Reference:
24 Interview Questions & Answers for Hadoop MapReduce developers
QUESTION: 91
You
need to create a GUI application to help your company's sales people
add and edit customer information. Would HDFS be appropriate for this
customer information file?
- Yes, because HDFS is optimized for random access writes.
- Yes, because HDFS is optimized for fast retrieval of relatively small amounts of data.
- No, because HDFS can only be accessed by MapReduce applications.
- No, because HDFS is optimized for write-once, streaming access for relatively large files.
Answer: D
Explanation:
HDFS is designed to support very large files. Applications that are compatible with
HDFS
are those that deal with large data sets. These applications write
their data only once but they read it one or more times and require
these reads to be satisfied at streaming speeds. HDFS supports
write-once-read-many semantics on files. Reference:
24
Interview Questions & Answers for Hadoop MapReduce developers, What
is HDFS ? How it is different from traditional file systems?
QUESTION: 92
Which of the following describes how a client reads a file from HDFS?
- The client queries the NameNode for the block location(s). The NameNode returns the blocklocation(s) to the client. The client reads the data directly off the DataNode(s).
- The client queries all DataNodes in parallel. The DataNode that contains the requested data responds directly to the client. The client reads the data directly off the DataNode.
- The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block locations. The DataNodes respond to theNameNode, and the NameNode redirects the client to the DataNode that holds the requested data block(s). The client then reads the data directly off the DataNode.
- The client contacts the NameNode for the block location(s). The NameNode contacts the DataNode that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the client.
Answer: C
Explanation:
The
Client communication to HDFS happens using Hadoop HDFS API. Client
applications talk to the NameNode whenever they wish to locate a file,
or when they want to add/copy/move/delete a file on HDFS. The NameNode
responds the successful requests by returning a list of relevant
DataNode servers where the data lives. Client applications can talk
directly to a DataNode, once the NameNode has provided the location of
the data.
Reference:
24 Interview Questions & Answers for Hadoop MapReduce developers, How the Client communicates with HDFS?
QUESTION: 93
Which of the following statements best describes how a large (100 GB) file is stored in HDFS?
- The file is divided into variable size blocks, which are stored on multiple data nodes. Each block is replicated three times by default.
- The file is replicated three times by default. Each copy of the file is stored on a separate datanodes.
- The master copy of the file is stored on a single datanode. The replica copies are divided into fixed-size blocks, which are stored on multiple datanodes.
- The file is divided into fixed-size blocks, which are stored on multiple datanodes. Each block is replicated three times by default. Multiple blocks from the same file might reside on the same datanode.
- The file is divided into fixed-size blocks, which are stored on multiple datanodes. Each block is replicated three times by default. HDFS guarantees that different blocks from the same file are never on the same datanode.
Answer: E
Explanation:
HDFS
is designed to reliably store very large files across machines in a
large cluster. It storeseach file as a sequence of blocks; all blocks in
a file except the last block are the same size. The blocks of a file
are replicated for fault tolerance. The block size and replication
factor are configurable per file. An application can specify
the
number
of replicas of a file. The replication factor can be specified at file
creation time and can be changed later. Files in HDFS are write-once and
have strictly one writer at any time. The NameNode makes all decisions
regarding replication of blocks. HDFS uses rack-aware replica placement
policy. In default configuration there are total 3 copies of a datablock
on HDFS, 2 copies are stored on datanodes on same rack and 3rd copy on a
different rack.
Reference:
24 Interview Questions & Answers for Hadoop MapReduce developers , How the HDFS Blocks are replicated?
QUESTION: 94
Your
cluster has 10 DataNodes, each with a single 1 TB hard drive. You
utilize all your disk capacity for HDFS, reserving none for MapReduce.
You implement default replication settings. What is the storage capacity
of your Hadoop cluster (assuming no compression)?
- about 3 TB
- about 5 TB
- about 10 TB
- about 11 TB
Answer: A
Explanation:
In
default configuration there are total 3 copies of a datablock on HDFS, 2
copies are stored on datanodes on same rack and 3rd copy on a different
rack.
Note:
HDFS is designed to reliably store very large files across machines in a
large cluster. It stores each file as a sequence of blocks; all blocks
in a file except the last block are the same size. The blocks of a file
are replicated for fault tolerance. The block size and replication
factor are configurable per file. An application can specify the number
of replicas of a file. The replication factor can be specified at file
creation time and can be changed later. Files in HDFS are write-once and
have strictly one writer at any time. The NameNode makes all decisions
regarding replication of blocks. HDFS uses rack-aware replica placement
policy.
Reference:
24 Interview Questions & Answers for Hadoop MapReduce developers , How the HDFS Blocks are replicated?
Cloudera CCD-470 Exam (Cloudera Certified Developer for Apache Hadoop CDH4 Upgrade (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 Professional program (CCP)
The industry's most demanding performance-based certifications, CCP evaluates and recognizes a candidate's mastery of the technical skills most sought after by employers.
CCP Data Engineer
CCP Data Engineers possesses the skills to develop reliable, autonomous, scalable data pipelines that result in optimized data sets for a variety of workloads.
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Named one of the top five big data certifications, CCP Data Scientists have demonstrated the skills of an elite group of specialists working with big data. Candidates must prove their abilities under real-world conditions, designing and developing a production-ready data science solution that is peer-evaluated for its accuracy, scalability, and robustness.
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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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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
Performance-Based
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.
Verified
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-470
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