CCD-333, Cloudera Certified Developer for Apache Hadoop examination is planned to check a
applicants confidence with the ideas and abilities in Cloudera developer
software. Applicants need to prepare before appearing in the examination.
The CCD-333 exam
is applicants who want to acquire Cloudera
Certified Developer for Apache Hadoop
·
Number of queries: 60
·
Time permitted: 90 minutes
·
Minimum score: 67 percent
·
Languages: English and Japanese
Topics covered in this examination:
Center Hadoop ideas
Identify and recognize Apache
Hadoop daemons and how they work both in data processing and storage. Get
acquainted how Apache Hadoop uses data locality. Specified a large data
situation, find out the disputes to big-scale computational replicas and how
allocated systems try to conquer different challenges caused by the situation.
Saving Files in Hadoop
Examine the challenges and
benefits of the HDFS architecture, containing how HDFS executes file sizes, block
abstraction and block sizes. Get acquainted default storage requirements and
replication values for duplication. Establish how HDFS stores, writes and reads
files.
Job submission and configuration
Create correct job
configuration limits, containing by use of appropriate and JobConf properties.
Recognize the right actions for MapReduce work submission.
Job implementation surrounding
Specified a MapReduce job,
establish the lifecycle of a Reducer and the lifecycle of a Mapper. Get
acquainted the major fault liberality standards at occupation in a MapReduce
job. Classify the function of Apache Hadoop Methods, Interfaces and Classes.
Get acquainted how tentative implementation uses variations in machine
capabilities and configurations in a equivalent surrounding; when and how it
runs.
Input and Output
Specified an example job,
determine and analyze the right InputFormat and OutputFormat to choose based on
job necessities. Get acquainted the function of the RecordReader, and of series
compression and files.
Job Lifecycle
Examine the order of processes
in a MapReduce job, how combiners and partitioners work, how data travels from
place to place and the shuffle and sort procedure.
Data procedures
Examine and establish the
connection of input keys to output keys in conditions of number and type, the
arrangement of values and the arrangement of keys. Specified examples identify
the number, input data, sort, and significance of emitted values and keys from
the Mappers as well as the emitted information from each Reducer and the figure
and contents of the outcome file.
Key and Value Types
Specified a situation, examine
and establish which of Hadoop’s data kinds for values and keys are suitable for
the work. Get acquainted value types and common key in the MapReduce framework
and the boundaries they execute.
Design Patterns and Common Algorithms
Calculate whether an algorithm
is well-matched for appearance in MapReduce. Get acquainted limitations and
implementation and tactics for connecting datasets in MapReduce. Examine the
function of Counters and DistributedCache.
The Hadoop Ecosystem
Examine a workflow situation
and decide when and how to control ecosystems projects, containing Apache Pig,
Apache Hive, Oozie and Sqoop. Get acquainted how Hadoop Streaming may implement
to a job workflow.
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