「信仰は偉大な感情で、創造の力になれます。」とゴーリキーは述べました。私の夢は最高のIT専門家になることです。 すると、あなたがいつでも最新のHDPCD資格模擬試験情報を持つことができます。それに、万一の場合、問題集を利用してからやはり試験に失敗すれば、NewValidDumpsは全額返金のことを約束します。 最近、HortonworksのHDPCD資格模擬試験は非常に人気のある認定試験です。

HDP Certified Developer HDPCD この重要な認証資格をもうすでに手に入れましたか。

HDP Certified Developer HDPCD資格模擬 - Hortonworks Data Platform Certified Developer あなたは試験の最新バージョンを提供することを要求することもできます。 NewValidDumpsのHDPCD 日本語版試験勉強法問題集があなたに適するかどうかを確認したいなら、まず問題集のデモをダウンロードして体験してください。難しいHDPCD 日本語版試験勉強法認定試験に合格したいなら、試験の準備をするときに関連する参考書を使わないとダメです。

しかし、HDPCD資格模擬認定試験を受けて資格を得ることは自分の技能を高めてよりよく自分の価値を証明する良い方法ですから、選択しなければならならないです。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。

Hortonworks HDPCD資格模擬 - 試験に失敗したら、全額で返金する承諾があります。

HortonworksのHDPCD資格模擬試験は大変です。あなたは復習資料に悩んでいるかもしれません。我々NewValidDumpsの提供するHortonworksのHDPCD資格模擬ソフトを利用して自分の圧力を減少しましょう。我々のチームは複雑な問題集を整理するに通じて、毎年の試験の問題を分析して最高のHortonworksのHDPCD資格模擬ソフトを作成します。今まで、我々は更新を努力しています。ご購入した後の一年間で、HortonworksのHDPCD資格模擬試験が更新されたら、あなたを理解させます。

すべては豊富な内容があって各自のメリットを持っています。あなたは各バーションのHortonworksのHDPCD資格模擬試験の資料をダウンロードしてみることができ、あなたに一番ふさわしいバーションを見つけることができます。

HDPCD PDF DEMO:

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

QUESTION NO: 2
Which one of the following statements describes the relationship between the NodeManager and the ApplicationMaster?
A. The ApplicationMaster starts the NodeManager in a Container
B. The NodeManager requests resources from the ApplicationMaster
C. The ApplicationMaster starts the NodeManager outside of a Container
D. The NodeManager creates an instance of the ApplicationMaster
Answer: D

QUESTION NO: 3
For each input key-value pair, mappers can emit:
A. As many intermediate key-value pairs as designed. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous).
B. As many intermediate key-value pairs as designed, but they cannot be of the same type as the input key-value pair.
C. One intermediate key-value pair, of a different type.
D. One intermediate key-value pair, but of the same type.
E. As many intermediate key-value pairs as designed, as long as all the keys have the same types and all the values have the same type.
Answer: E
Explanation:
Mapper maps input key/value pairs to a set of intermediate key/value pairs.
Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs.
Reference: Hadoop Map-Reduce Tutorial

QUESTION 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.HCatOutputFormat
B. org.apache.hcatalog.pig.HCatStorer
C. No special class is needed for a Pig script to store data in an HCatalog table
D. Pig scripts cannot use an HCatalog table
Answer: B

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