Cloudera Hadoop管理员（CCAH）＆开发者（CCA）认证大纲
Cloudera Certified Administrator for Apache 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 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
CCA Spark and Hadoop Developer Exam (CCA175)
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)
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:
• 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
以上，有疑问可加Q1438118790询问
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