そのデモはHDPCD日本語練習問題試験資料の一部を含めています。私たちは本当にお客様の貴重な意見をHDPCD日本語練習問題試験資料の作りの考慮に入れます。おそらく、君たちは私たちのHDPCD日本語練習問題試験資料について何も知らないかもしれません。 NewValidDumpsのHortonworksのHDPCD日本語練習問題試験トレーニング資料を持っていたら、試験に対する充分の準備がありますから、安心に利用したください。NewValidDumpsは優れたIT情報のソースを提供するサイトです。 NewValidDumpsはHortonworksのHDPCD日本語練習問題の認定試験の受験生にとっても適合するサイトで、受験生に試験に関する情報を提供するだけでなく、試験の問題と解答をはっきり解説いたします。

HDPCD日本語練習問題認定試験に合格することは難しいようですね。

NewValidDumpsが提供したHortonworksのHDPCD - Hortonworks Data Platform Certified Developer日本語練習問題の問題集は真実の試験に緊密な相似性があります。 一回だけでHortonworksのHDPCD 勉強資料試験に合格したい？NewValidDumpsは君の欲求を満たすために存在するのです。NewValidDumpsは君にとってベストな選択になります。

もし君がHortonworksのHDPCD日本語練習問題に参加すれば、良い学習のツルを選ぶすべきです。HortonworksのHDPCD日本語練習問題認定試験はIT業界の中でとても重要な認証試験で、合格するために良い訓練方法で準備をしなければなりません。。

Hortonworks HDPCD日本語練習問題 - 我々の誠意を信じてください。

現在でHortonworksのHDPCD日本語練習問題試験を受かることができます。NewValidDumpsにHortonworksのHDPCD日本語練習問題試験のフルバージョンがありますから、最新のHortonworksのHDPCD日本語練習問題のトレーニング資料をあちこち探す必要がないです。NewValidDumpsを利用したら、あなたはもう最も良いHortonworksのHDPCD日本語練習問題のトレーニング資料を見つけたのです。弊社の質問と解答を安心にご利用ください。あなたはきっとHortonworksのHDPCD日本語練習問題試験に合格できますから。

自分のIT業界での発展を希望したら、HortonworksのHDPCD日本語練習問題試験に合格する必要があります。HortonworksのHDPCD日本語練習問題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日本語練習問題試験に合格することができます。

HDPCD PDF DEMO:

QUESTION NO: 1
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: 2
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: 3
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: 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 ?

SAP C-TADM-23-JPN - この認証は自分のキャリアを強化することができ、自分が成功に近づかせますから。 あなたは自分の望ましいHortonworks Microsoft DP-500問題集を選らんで、学びから更なる成長を求められます。 これは当社が提供したHortonworksのEC-COUNCIL 212-81トレーニング資料が本当に効果的なものということを証明しました。 短時間でEMC D-GAI-F-01試験に一発合格したいなら、我々社のHortonworksのEMC D-GAI-F-01資料を参考しましょう。 Microsoft AZ-500 - それはあなたがいつでも最新の試験資料を持てるということです。

Updated: May 27, 2022