Cloudera Hadoop Administrator Ccah; developer CCA-175 Exam Outline

Source: Internet
Author: User
Tags sqoop

Cloudera Certified Administrator forapache Hadoop (CCA-500)
Number of Questions:Questions
Time Limit:minutes
Passing Score:70%
Language:中文版, Japanese

Exam Sections and Blueprint

1. HDFS (17%)

  • Describe the function of HDFS daemons

  • Describe the normal operation of a Apache Hadoop cluster, both in data storage and in data processing

  • Identify current features of computing systems, 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 the change and the How on order to migrate a cluster from the MapReduce version 1 (MRv1) to mapredu CE version 2 (MRV2) running on YARN

3. Hadoop Cluster Planning (16%)

  • Principal points to consider in choosing the hardware and operating systems to host a 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 n Etwork design components for a given scenario

4. Hadoop Cluster installation andadministration (25%)

  • Given a scenario, identify how the cluster would 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 the CDH 5, including (and not limited): 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 the 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 the 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

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Cloudera Hadoop Administrator Ccah; developer CCA-175 Exam Outline

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