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CCA-410 - Cloudera Certified Administrator for Apache Hadoop (CCAH) - Dump Information

Vendor : Cloudera
Exam Code : CCA-410
Exam Name : Cloudera Certified Administrator for Apache Hadoop (CCAH)
Questions and Answers : 97 Q & A
Updated On : June 27, 2017
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CCA-410 Questions and Answers

CCA-410


Reference:

CONFIGURATION PARAMETERS DFS.BLOCK.SIZE


QUESTION: 91

Which daemons instantiates JVM’s to perform mapreduce processing in a cluster running mapreduce v1 (MRv1)?


  1. Nodemanager

  2. applicationmanager

  3. Applicationmaster

  4. Tasktracker

  5. Jobtracker

  6. Datanode

  7. Namenode

  8. Resourcemanager


Answer: D


Explanation:

A TaskTracker is a slave node daemon in the cluster that accepts tasks (Map, Reduce and Shuffle operations) from a JobTracker. There is only One Task Tracker process run on any hadoop slave node. Task Tracker runs on its own JVM process. Every TaskTracker is configured with a set of slots, these indicate the number of tasks that it

can accept. The TaskTracker starts a separate JVM processes to do the actual work

(called as Task Instance) this is to ensure that process failure does not take down the task tracker.


QUESTION: 92

You set mapred.tasktracker.reduce.tasks.maximum to a value of 4 on a cluster running mapreduce v1 (MRv1). How many reducers will run for any given job?


  1. Maximum of 4 reducers, but the actual number of reducers that run for any given job is based on the volume of input data

  2. a maximum of 4 reducer’s, but the actual number of reducer’s that run for any given job is based on the volume intermediate data.

  3. Four reducer’s will run. Once set by the cluster administrator, this parameter can’t be overridden

  4. The number of reducer’s for any given job is set by the developer


Answer: B


Explanation:

mapred.tasktracker.reduce.tasks.maximum The maximum number of reduce tasks that will be run simultaneously by a task tracker. A reducer uses intermediate data as input.

Note:

  • The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes.

  • Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer.


    QUESTION: 93

    You configure your hadoop development cluster with both mapreduce frameworks, mapreduceV1 (MRv1) and mapreduce v2 (MRv2/YARN). You plan to run only one set of mapreduce daemons at a time in this development environment (running both simultaneously results in an unsuitable cluster but configure in both moving between them is fine). Which two mapreduce (capitation) daemons do you need to configure to run on your masternodes?


    1. Containermanager

    2. Nodemanager

    3. Journalnode

    4. Applicationmaster

    5. Resourcemanager

    6. Jobtracker


    Answer: E, F


    Explanation:

    Jobtracker for mapreduce V1 (MRv1) and ResourceManager for mapreduce v2 (MRv2/YARN).

  • Configure YARN daemons

    If you have decided to run YARN, you must configure the following services: ResourceManager (on a dedicated host) and NodeManager (on every host where you plan to run MapReduce v2 jobs).


  • JobTracker is the daemon service for submitting and tracking MapReduce V1 (MRv1) jobs in Hadoop.

    Note:

  • Make sure you are not trying to run MRv1 and YARN on the same set of nodes at the same time. This is not supported; it will degrade performance and may result in an unstable cluster deployment.

  • MapReduce has undergone a complete overhaul and CDH4 now includes MapReduce 2.0 (MRv2). The fundamental idea of MRv2's YARN architecture is to split up the two primary responsibilities of the JobTracker — resource management and job scheduling/monitoring — into separate daemons: a global ResourceManager (RM) and per-application ApplicationMasters (AM).

    Incorrect:

    Not C: JournalNode is for Quorum implementation.


    QUESTION: 94

    Using hadoop’s default settings, how much data will be able to store on your hadoop cluster if it is has 12 nodes with 4TB raw diskspace per node allocated to HDFS storage?


    1. Approximately 3TB

    2. Approximately 12TB

    3. Approximately 16TB

    4. Approximately 48TB


    Answer: C


    Explanation:

    Default replication value is 3. The following amount of data can be stored: 12 x 4 /3 = 16 TB


    QUESTION: 95

    When requesting a file, how does HDFS retrieves the blocks associated with that file?


    1. the client polls the datanodes for the block ID’s

    2. the namenode requires the datanode for the block ID’s

    3. The namenode reads the block ID’s from memory

    4. the namenode reads the block ID’s from disk


    Answer: D


    Explanation:

    Here is how a client RPC request to the Hadoop HDFS NameNode flows through the NameNode. The Hadoop NameNode receives requests from HDFS clients in the form of Hadoop RPC requests over a TCP connection. Typical client requests include mkdir, getBlockLocations, create file, etc. Remember – HDFS separates metadata from actual file data, and that the NameNode is the metadata server. Hence, these requests are pure metadata requests – no data transfer is involved.


    QUESTION: 96

    What four functions do scheduling algorithms perform on hadoop cluster?


    1. Run jobs at periodic times of the day

    2. Reduce the job latencies in environment with multiple jobs of different sizes

    3. Allow multiple users to share clusters in a predictable policy-guided manner

    4. support the implementation of service-level agreements for multiple cluster users

    5. allow short jobs to complete even when large, long jobs (consuming a lot of resources are running)

    6. Reduce the total amount of computation necessary to complete a job.

    7. Ensure data locality by ordering map tasks so that they run on data local maps slots


    Answer: A, B, E, F


    Explanation:

    Hadoop schedulers Since the pluggable scheduler was implemented, several scheduler algorithms have been developed for it. / FIFO scheduler The original scheduling algorithm that was integrated within the JobTracker was called FIFO. In FIFO scheduling, a JobTracker pulled jobs from a work queue, oldest job first. This schedule had no concept of the priority or size of the job, but the approach was simple to implement and efficient. / Fair scheduler The core idea behind the fair share scheduler was to assign resources to jobs such that on average over time, each job gets an equal share of the available resources. The result is that jobs that require less time are able to access the CPU and finish intermixed with the execution of jobs that require more time to execute. This behavior allows for some interactivity among Hadoop jobs and permits greater responsiveness of the Hadoop cluster to the variety of job types submitted. The fair scheduler was developed by Facebook. / Capacity scheduler The capacity scheduler shares some of the principles of the fair scheduler but has distinct differences, too. First, capacity scheduling was defined for large clusters, which may have multiple, independent consumers and target applications.


    For this reason, capacity scheduling provides greater control as well as the ability to provide a minimum capacity guarantee and share excess capacity among users. The capacity scheduler was developed by Yahoo!. In capacity scheduling, instead of pools, several queues are created, each with a configurable number of map and reduce slots. Each queue is also assigned a guaranteed capacity (where the overall capacity of the cluster is the sum of each queue's capacity).

  • The introduction of the pluggable scheduler was yet another evolution in cluster computing with Hadoop. The pluggable scheduler permits the use (and development) of schedulers optimized for the particular workload and application. The new schedulers have also made it possible to create multi-user data warehouses with Hadoop, given the ability to share the overall Hadoop infrastructure with multiple users and organizations.


QUESTION: 97

What happens when a map task crashes while running a mapreduce job?


  1. The job immediately fails

  2. The tasktracker closes the JVM instance and restarts

  3. The jobtracker attempts to re-run the task on the same node

  4. the jobtracket attempts to re-run the task on a different node


Answer: D


Explanation:

When the jobtracker is notified of a task attempt that has failed (by the tasktracker’s heartbeat call), it will reschedule execution of the task. The jobtracker will try to avoid rescheduling the task on a tasktracker where it has previously failed.


Cloudera CCA-410 Exam (Cloudera Certified Administrator for Apache Hadoop (CCAH)) 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 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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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’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)
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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:
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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 CCA-410

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