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NO.1 What two processes must you do if you are running a Hadoop cluster with a single NameNode
and six DataNodes, and you want to change a configuration parameter so that it affects all six
DataNodes.
A. You must modify the configuration files on each of the six SataNodes machines
B. You must modify the configuration files on the NameNode only. DataNodes read their
configuration from the master nodes
C. You must restart the NameNode daemon to apply the changes to the cluster
D. You don't need to restart any daemon, as they will pick up changes automatically
E. You must restart all six DatNode daemon to apply the changes to the cluster
Answer: A,C

CCA-500 Question   CCA-500 Tool   

NO.2 Assuming you're not running HDFS Federation, what is the maximum number of NameNode
daemons you should run
on your cluster in order to avoid a "split-brain" scenario with your NameNode when running HDFS
High Availability
(HA) using Quorum-based storage?
A. Unlimited. HDFS High Availability (HA) is designed to overcome limitations on the number of
NameNodes you can deploy
B. Two active NameNodes and on Standby NameNode
C. Two active NameNodes and two Standby NameNodes
D. One active NameNode and one Standby NameNode
Answer: D

CCA-500 Technical   

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Exam Code: CCA-500

Exam Name: Cloudera Certified Administrator for Apache Hadoop (CCAH)

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CCA-500 Reliable Visual Cert Test Total Q&A: 60 Questions and Answers

Last Update: 2017-04-24

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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

Disclaimer: These exam preparation pages are intended to provide information about the objectives covered by each exam, related resources, and recommended reading and courses. The material contained within these pages is not intended to guarantee a passing score on any exam. Cloudera recommends that a candidate thoroughly understand the objectives for each exam and utilize the resources and training courses recommended on these pages to gain a thorough understand of the domain of knowledge related to the role the exam evaluates.