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标题: Cloudera Hadoop管理员（CCAH）＆开发者（CCA）认证大纲 [查看完整版帖子] [打印本页]

时间: 2016-3-23 14:42

作者: souwin 标题: Cloudera Hadoop管理员（CCAH）＆开发者（CCA）认证大纲

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 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 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, 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 andAdministration (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 files
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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

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