Cloudera Hadoop 管理员CCAH；开发者CCA-175考试大纲

顺德公农庄

　　Cloudera Certified Administrator forApache Hadoop (CCA-500)
　　Number of Questions: 60 questions
　　Time Limit: 90 minutes
　　Passing Score: 70%
　　Language: English, Japanese
　　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 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 and MapReduce version 2 (MRv2)(17%)

• 　　Understand how upgrading a cluster from Hadoop     1 to Hadoop 2 affects cluster settings
  
• 　　Understand how to deploy MapReduce v2 (MRv2 /     YARN), including all YARN daemons
  
• 　　Understand basic design strategy for MapReduce     v2 (MRv2)
  
• 　　Determine how YARN handles resource     allocations
  
• 　　Identify the workflow of MapReduce 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,    >
  
• 　　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,>
  
• 　　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>
  

　　4. Hadoop Cluster Installation andAdministration (25%)

• 
  　　Given a scenario,>
  
• 　　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
  

　　CCA Spark and Hadoop Developer Exam(CCA175)
　　Number of Questions: 10–12performance-based (hands-on) tasks on CDH5 cluster. See below for full clusterconfiguration
　　Time Limit: 120 minutes
　　Passing Score: 70%
　　Language: English, Japanese (forthcoming)
　　Required Skills
　　Data Ingest
　　The skills to transfer data between external systemsand 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 storedin 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:

• 　　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 inthe 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
  

　　以上，有疑问可加Q1438118790询问