Cloudera Hadoop Administrator Ccah; developer CCA-175 Exam Outline

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