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試験番号:HDPCD問題集
試験科目:Hortonworks Data Platform Certified Developer
最近更新時間:2017-01-21
問題と解答:全110問 HDPCD 合格記
100%の返金保証。1年間の無料アップデート。

>> HDPCD 合格記

 

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 NodeManager requests resources from the ApplicationMaster
C. The ApplicationMaster starts the NodeManager outside of a Container
D. The ApplicationMaster starts the NodeManager in a Container
Answer: A

HDPCD 準備   

NO.2 In a MapReduce job with 500 map tasks, how many map task attempts will there be?
A. At least 500.
B. Between 500 and 1000.
C. At most 500.
D. Exactly 500.
E. It depends on the number of reduces in the job.
Answer: A

HDPCD 受験期   
Explanation:
From Cloudera Training Course:
Task attempt is a particular instance of an attempt to execute a task
- There will be at least as many task attempts as there are tasks
- If a task attempt fails, another will be started by the JobTracker
- Speculative execution can also result in more task attempts than completed tasks

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 on the TaskTracker node running the Mapper that spill over and are
written into HDFS.
B. Intermediate data in streamed across the network from Mapper to the Reduce and is never
written to disk.
C. Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker
node running the Reducer
D. Into in-memory buffers that spill over to the local file system of the TaskTracker node running the
Mapper.
E. Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are
written into HDFS.
Answer: D

HDPCD 答案   
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 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. 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.
B. There is no difference in output between the two settings.
C. 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.
D. 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.
Answer: C

HDPCD 通信   
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.

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