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NO.1 Which one of the following statements describes the relationship between the NodeManager
and the ApplicationMaster?
A. The NodeManager creates an instance of the ApplicationMaster
B. The ApplicationMaster starts the NodeManager in a Container
C. The ApplicationMaster starts the NodeManager outside of a Container
D. The NodeManager requests resources from the ApplicationMaster
Answer: A

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NO.2 Which one of the following classes would a Pig command use to store data in a table defined in
HCatalog?
A. Pig scripts cannot use an HCatalog table
B. No special class is needed for a Pig script to store data in an HCatalog table
C. org.apache.hcatalog.pig.HCatStorer
D. org.apache.hcatalog.pig.HCatOutputFormat
Answer: C

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NO.3 You have just executed a MapReduce job.
Where is intermediate data written to after being emitted from the Mapper's map method?
A. Into in-memory buffers that spill over to the local file system of the TaskTracker node running the
Mapper.
B. Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker
node running the Reducer
C. Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are
written into HDFS.
D. Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are
written into HDFS.
E. Intermediate data in streamed across the network from Mapper to the Reduce and is never
written to disk.
Answer: A
Explanation:
The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each
individual mapper nodes. This is typically a temporary directory location which can be setup in config
by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.
Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, Where is the
Mapper Output (intermediate kay-value data) stored ?

NO.4 MapReduce v2 (MRv2/YARN) splits which major functions of the JobTracker into separate
daemons? Select two.
A. MapReduce metric reporting
B. Managing tasks
C. Managing file system metadata
D. Job coordination between the ResourceManager and NodeManager
E. Heath states checks (heartbeats)
F. Job scheduling/monitoring
G. Launching tasks
H. Resource management
Answer: F,H

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Explanation:
The fundamental idea of MRv2 is to split up the two major functionalities of
the JobTracker, resource management and job scheduling/monitoring, into separate
daemons. The idea is to have a global ResourceManager (RM) and per-application
ApplicationMaster (AM). An application is either a single job in the classical sense of Map-
Reduce jobs or a DAG of jobs.
Note:
The central goal of YARN is to clearly separate two things that are unfortunately smushed
together in current Hadoop, specifically in (mainly) JobTracker:
/ Monitoring the status of the cluster with respect to which nodes have which resources
available. Under YARN, this will be global.
/ Managing the parallelization execution of any specific job. Under YARN, this will be done separately
for each job.
Reference: Apache Hadoop YARN - Concepts & Applications

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HDPCD試験番号:HDPCD問題集
試験科目:Hortonworks Data Platform Certified Developer
最近更新時間:2016-09-28
問題と解答:全110問 HDPCD 本番
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