ndividuals
who achieve Cloudera Certified Developer for Apache Hadoop (CCDH) have
demonstrated their technical knowledge, skill, and ability to write, maintain,
and optimize Apache Hadoop Java MapReduce development projects.
Cloudera
Certified Developer for Apache Hadoop (CCD-410)
Number of Questions: 50 - 60 live questions
Time Limit: 90 minutes
Passing Score: 70%
Language: English, Japanese
Price: USD $295
Difficulty: Questions are delivered dynamically and based on difficulty ratings so that each candidate receives an exam at a consistent level. Each test also includes a small number of unscored, experimental (beta) questions.
Number of Questions: 50 - 60 live questions
Time Limit: 90 minutes
Passing Score: 70%
Language: English, Japanese
Price: USD $295
Difficulty: Questions are delivered dynamically and based on difficulty ratings so that each candidate receives an exam at a consistent level. Each test also includes a small number of unscored, experimental (beta) questions.
Exam Sections and Blueprint
Each
candidate receives 50 – 60 questions. Questions are delivered dynamically and
based on difficulty ratings so that each candidate receives an exam at a
consistent level. Each test also includes a number of unscored, experimental
(beta) questions.
Infrastructure: Hadoop
components that are outside the concerns of a particular MapReduce job that a
developer needs to master (25%)
Data Management: Developing, implementing, and executing commands to properly manage the full data lifecycle of a Hadoop job (30%)
Job Mechanics: The processes and commands for job control and execution with an emphasis on the process rather than the data (25%)
Querying: Extracting information from data (20%)
Data Management: Developing, implementing, and executing commands to properly manage the full data lifecycle of a Hadoop job (30%)
Job Mechanics: The processes and commands for job control and execution with an emphasis on the process rather than the data (25%)
Querying: Extracting information from data (20%)
1. Infrastructure Objectives
- Recognize and identify Apache
Hadoop daemons and how they function both in data storage and processing.
- Understand how Apache Hadoop
exploits data locality.
- Identify the role and use of
both MapReduce v1 (MRv1) and MapReduce v2 (MRv2 / YARN) daemons.
- Analyze the benefits and
challenges of the HDFS architecture.
- Analyze how HDFS implements
file sizes, block sizes, and block abstraction.
- Understand default replication
values and storage requirements for replication.
- Determine how HDFS stores,
reads, and writes files.
- Identify the role of Apache
Hadoop Classes, Interfaces, and Methods.
- Understand how Hadoop Streaming
might apply to a job workflow.
2. Data Management Objectives
- Import a database table into
Hive using Sqoop.
- Create a table using Hive
(during Sqoop import).
- Successfully use key and value
types to write functional MapReduce jobs.
- Given a MapReduce job,
determine the lifecycle of a Mapper and the lifecycle of a Reducer.
- Analyze and determine the
relationship of input keys to output keys in terms of both type and
number, the sorting of keys, and the sorting of values.
- Given sample input data,
identify the number, type, and value of emitted keys and values from the
Mappers as well as the emitted data from each Reducer and the number and
contents of the output file(s).
- Understand implementation and
limitations and strategies for joining datasets in MapReduce.
- Understand how partitioners and
combiners function, and recognize appropriate use cases for each.
- Recognize the processes and
role of the the sort and shuffle process.
- Understand common key and value
types in the MapReduce framework and the interfaces they implement.
- Use key and value types to
write functional MapReduce jobs.
3. Job Mechanics Objectives
- Construct proper job
configuration parameters and the commands used in job submission.
- Analyze a MapReduce job and
determine how input and output data paths are handled.
- Given a sample job, analyze and
determine the correct InputFormat and OutputFormat to select based on job
requirements.
- Analyze the order of operations
in a MapReduce job.
- Understand the role of the
RecordReader, and of sequence files and compression.
- Use the distributed cache to
distribute data to MapReduce job tasks.
- Build and orchestrate a
workflow with Oozie.
4. Querying Objectives
- Write a MapReduce job to
implement a HiveQL statement.
- Write a MapReduce job to query
data stored in HDFS.
Disclaimer: This
exam preparation page is intended to provide information about the objectives
covered by each exam. The material contained within these pages is not intended
to guarantee a passing score on any exam. Cloudera recommends that a candidate
thoroughly understand the objectives for each exam and utilize the resources
and training courses recommended on these pages to gain a thorough understand
of the domain of knowledge related to the role the exam evaluates.
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