最近、HortonworksのHDPCD問題無料試験は非常に人気のある認定試験です。あなたもこの試験の認定資格を取得したいのですか。さて、はやく試験を申し込みましょう。 NewValidDumpsのHortonworksのHDPCD問題無料試験トレーニング資料は実践の検証に合格したソフトで、手に入れたらあなたに最も向いているものを持つようになります。NewValidDumpsのHortonworksのHDPCD問題無料試験トレーニング資料を購入する前に、無料な試用版を利用することができます。 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。

HDP Certified Developer HDPCD もちろんありますよ。

HDP Certified Developer HDPCD問題無料 - Hortonworks Data Platform Certified Developer その資料は最完全かつ最新で、合格率が非常に高いということで人々に知られています。 君がHortonworksのHDPCD 対応資料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 対応資料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。

この問題集はあなたが少ない労力で最高の結果を取得することができます。NewValidDumpsはIT認定試験を受験した多くの人々を助けました。また、受験生からいろいろな良い評価を得ています。

解決法はHortonworks HDPCD問題無料問題集は購入することです。

近年、IT領域で競争がますます激しくなります。IT認証は同業種の欠くことができないものになりました。あなたはキャリアで良い昇進のチャンスを持ちたいのなら、NewValidDumpsのHortonworksのHDPCD問題無料「Hortonworks Data Platform Certified Developer」試験トレーニング資料を利用してHortonworksの認証の証明書を取ることは良い方法です。現在、HortonworksのHDPCD問題無料認定試験に受かりたいIT専門人員がたくさんいます。NewValidDumpsの試験トレーニング資料はHortonworksのHDPCD問題無料認定試験の１００パーセントの合格率を保証します。

おそらく、君たちは私たちのHDPCD問題無料試験資料について何も知らないかもしれません。でも、私たちのHDPCD問題無料試験資料のデモをダウンロードしてみると、全部わかるようになります。

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
Given the following Hive commands:
Which one of the following statements Is true?
A. The file mydata.txt is copied to a subfolder of /apps/hive/warehouse
B. The file mydata.txt is moved to a subfolder of /apps/hive/warehouse
C. The file mydata.txt is copied into Hive's underlying relational database 0.
D. The file mydata.txt does not move from Its current location in HDFS
Answer: A

QUESTION NO: 4
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: 5
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

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Updated: May 27, 2022