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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:2017-02-08
問題と解答:全110問 HDPCD 日本語受験攻略
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>> HDPCD 日本語受験攻略

 

NO.1 MapReduce v2 (MRv2/YARN) splits which major functions of the JobTracker into separate
daemons? Select two.
A. Heath states checks (heartbeats)
B. Managing tasks
C. Launching tasks
D. MapReduce metric reporting
E. Job scheduling/monitoring
F. Job coordination between the ResourceManager and NodeManager
G. Managing file system metadata
H. Resource management
Answer: E,H

HDPCD 更新版   
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

NO.2 You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses
TextInputFormat: the mapper applies a regular expression over input values and emits key-values
pairs with the key consisting of the matching text, and the value containing the filename and byte
offset. Determine the difference between setting the number of reduces to one and settings the
number of reducers to zero.
A. There is no difference in output between the two settings.
B. With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances
of matching patterns are stored in a single file on HDFS.
C. With zero reducers, all instances of matching patterns are gathered together in one file on HDFS.
With one reducer, instances of matching patterns are stored in multiple files on HDFS.
D. With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one
reducer, all instances of matching patterns are gathered together in one file on HDFS.
Answer: D

HDPCD 技術者   
Explanation:
* It is legal to set the number of reduce-tasks to zero if no reduction is desired.
In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by
setOutputPath(Path). The framework does not sort the map-outputs before writing them out to the
FileSystem.
* Often, you may want to process input data using a map function only. To do this, simply set
mapreduce.job.reduces to zero. The MapReduce framework will not create any reducer tasks.
Rather, the outputs of the mapper tasks will be the final output of the job.
Note:
Reduce
In this phase the reduce(WritableComparable, Iterator, OutputCollector, Reporter) method
is called for each <key, (list of values)> pair in the grouped inputs.
The output of the reduce task is typically written to the FileSystem via
OutputCollector.collect(WritableComparable, Writable).
Applications can use the Reporter to report progress, set application-level status messages
and update Counters, or just indicate that they are alive.
The output of the Reducer is not sorted.

NO.3 For each intermediate key, each reducer task can emit:
A. One final key-value pair per value associated with the key; no restrictions on the type.
B. As many final key-value pairs as desired, as long as all the keys have the same type and all the
values have the same type.
C. As many final key-value pairs as desired, but they must have the same type as the intermediate
key-value pairs.
D. As many final key-value pairs as desired. There are no restrictions on the types of those key-value
pairs (i.e., they can be heterogeneous).
E. One final key-value pair per key; no restrictions on the type.
Answer: B

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Reference: Hadoop Map-Reduce Tutorial; Yahoo! Hadoop Tutorial, Module 4: MapReduce

NO.4 Which one of the following classes would a Pig command use to store data in a table defined in
HCatalog?
A. org.apache.hcatalog.pig.HCatStorer
B. org.apache.hcatalog.pig.HCatOutputFormat
C. Pig scripts cannot use an HCatalog table
D. No special class is needed for a Pig script to store data in an HCatalog table
Answer: A

HDPCD 解説   

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