弊社の資源はずっと改訂され、アップデートされていますから、緊密な相関関係があります。HortonworksのHDPCD認定資格の認証試験を準備しているあなたは、自分がトレーニングを選んで、しかも次の問題を受かったほうがいいです。弊社の試験問題はほとんど毎月で一回アップデートしますから、あなたは市場で一番新鮮な、しかも依頼できる良い資源を得ることができることを保証いたします。 NewValidDumpsの試験トレーニング資料はHortonworksのHDPCD認定資格認定試験の１００パーセントの合格率を保証します。近年、IT領域で競争がますます激しくなります。 それは我々はHortonworksのHDPCD認定資格問題集やHDPCD認定資格スタディガイドやHDPCD認定資格問題と解答がたくさんありますから。

HDP Certified Developer HDPCD ここには、私たちは君の需要に応じます。

HDP Certified Developer HDPCD認定資格 - Hortonworks Data Platform Certified Developer この認証を持っていたら、あなたは自分の夢を実現できます。 NewValidDumpsのHortonworksのHDPCD 日本語Pdf問題試験トレーニング資料は試験問題と解答を含まれて、豊富な経験を持っているIT業種の専門家が長年の研究を通じて作成したものです。その権威性は言うまでもありません。

信じないでしょうか。NewValidDumpsの試験問題集はそのような資料ですよ。はやく試してください。

Hortonworks HDPCD認定資格 - しかし、これは本当のことですよ。

自分のIT業界での発展を希望したら、HortonworksのHDPCD認定資格試験に合格する必要があります。HortonworksのHDPCD認定資格試験はいくつ難しくても文句を言わないで、我々NewValidDumpsの提供する資料を通して、あなたはHortonworksのHDPCD認定資格試験に合格することができます。HortonworksのHDPCD認定資格試験を準備しているあなたに試験に合格させるために、我々NewValidDumpsは模擬試験ソフトを更新し続けています。

では、どんな参考書は選べる価値を持っていますか。あなたが選んだのは、NewValidDumpsのHDPCD認定資格問題集ですか。

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