周りの多くの人は全部Hortonworks　HDPCDテスト問題集資格認定試験にパースしまして、彼らはどのようにできましたか。今には、あなたにNewValidDumpsを教えさせていただけませんか。我々社サイトのHortonworks　HDPCDテスト問題集問題庫は最新かつ最完備な勉強資料を有して、あなたに高品質のサービスを提供するのはHDPCDテスト問題集資格認定試験の成功にとって唯一の選択です。 NewValidDumpsを選ぶのは成功を選ぶのに等しいと言えます。質の良いHortonworksのHDPCDテスト問題集試験トレーニング資料が見つけられないので、まだ悩んでいますか。 そうすれば、あなたは簡単にHDPCDテスト問題集復習教材のデモを無料でダウンロードできます。

HDP Certified Developer HDPCD そうすれば、わかりやすく、覚えやすいです。

NewValidDumpsのHDPCD - Hortonworks Data Platform Certified Developerテスト問題集教材を購入したら、あなたは一年間の無料アップデートサービスを取得しました。 弊社の専門家は経験が豊富で、研究した問題集がもっとも真題と近づいて現場試験のうろたえることを避けます。Hortonworks HDPCD テスト模擬問題集認証試験を通るために、いいツールが必要です。

ところで、受験生の皆さんを簡単にIT認定試験に合格させられる方法がないですか。もちろんありますよ。NewValidDumpsの問題集を利用することは正にその最良の方法です。

Hortonworks HDPCDテスト問題集 - NewValidDumpsを選られば、成功しましょう。

NewValidDumpsのHortonworksのHDPCDテスト問題集試験トレーニング資料は豊富な経験を持っているIT専門家が研究したものです。君がHortonworksのHDPCDテスト問題集問題集を購入したら、私たちは一年間で無料更新サービスを提供することができます。もしHortonworksのHDPCDテスト問題集問題集は問題があれば、或いは試験に不合格になる場合は、全額返金することを保証いたします。

君はまずネットで無料な部分のHortonworks認証試験をダウンロードして現場の試験の雰囲気を感じて試験に上手になりますよ。HortonworksのHDPCDテスト問題集認証試験に失敗したら弊社は全額で返金するのを保証いたします。

HDPCD PDF DEMO:

QUESTION NO: 1
For each intermediate key, each reducer task can emit:
A. 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).
B. As many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs.
C. 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.
D. One final key-value pair per value associated with the key; no restrictions on the type.
E. One final key-value pair per key; no restrictions on the type.
Answer: C
Reference: Hadoop Map-Reduce Tutorial; Yahoo! Hadoop Tutorial, Module 4: MapReduce

QUESTION 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
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.

QUESTION NO: 3
Which best describes how TextInputFormat processes input files and line breaks?
A. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the beginning of the broken line.
B. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of both splits containing the broken line.
C. The input file is split exactly at the line breaks, so each RecordReader will read a series of complete lines.
D. Input file splits may cross line breaks. A line that crosses file splits is ignored.
E. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the end of the broken line.
Answer: A
Reference: How Map and Reduce operations are actually carried out

QUESTION NO: 4
In a MapReduce job with 500 map tasks, how many map task attempts will there be?
A. It depends on the number of reduces in the job.
B. Between 500 and 1000.
C. At most 500.
D. At least 500.
E. Exactly 500.
Answer: D
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

QUESTION NO: 5
You have just executed a MapReduce job.
Where is intermediate data written to after being emitted from the Mapper's map method?
A. Intermediate data in streamed across the network from Mapper to the Reduce and is never written to disk.
B. Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into HDFS.
C. Into in-memory buffers that spill over to the local file system of the TaskTracker node running the
Mapper.
D. Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node running the Reducer
E. Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into HDFS.
Answer: C
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 ?

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Updated: May 27, 2022