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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