NewValidDumpsのHortonworksのHDPCD最新知識トレーニング資料即ち問題と解答をダウンロードする限り、気楽に試験に受かることができるようになります。まだ困っていたら、我々の試用版を使ってみてください。ためらわずに速くあなたのショッピングカートに入れてください。 この試験に合格したら、あなたのITキャリアには明るい未来があるようになります。あなたを助けるために、我々のNewValidDumpsは真実かつ正確なトレーニング資料を提供します。 NewValidDumpsはあなたが首尾よく試験に合格することを助けるだけでなく、あなたの知識と技能を向上させることもできます。

HDP Certified Developer HDPCD あなた準備しましたか。

真剣にNewValidDumpsのHortonworks HDPCD - Hortonworks Data Platform Certified Developer最新知識問題集を勉強する限り、受験したい試験に楽に合格することができるということです。 あなたがする必要があるのは、問題集に出るすべての問題を真剣に勉強することです。この方法だけで、試験を受けるときに簡単に扱うことができます。

がむしゃらに試験に関連する知識を勉強しているのですか。それとも、効率が良い試験HDPCD最新知識参考書を使っているのですか。Hortonworksの認証資格は最近ますます人気になっていますね。

Hortonworks HDPCD最新知識 - 早速買いに行きましょう。

HDPCD最新知識認定試験は現在で本当に人気がある試験ですね。まだこの試験の認定資格を取っていないあなたも試験を受ける予定があるのでしょうか。確かに、これは困難な試験です。しかし、難しいといっても、高い点数を取って楽に試験に合格できないというわけではないです。では、まだ試験に合格するショートカットがわからないあなたは、受験のテクニックを知りたいですか。今教えてあげますよ。それはNewValidDumpsのHDPCD最新知識問題集を利用することです。

NewValidDumpsのHortonworksのHDPCD最新知識試験トレーニング資料はHortonworksのHDPCD最新知識認定試験を準備するのリーダーです。NewValidDumpsの HortonworksのHDPCD最新知識試験トレーニング資料は高度に認証されたIT領域の専門家の経験と創造を含めているものです。

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