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

     1. 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

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