今の社会の中で、ネット上で訓練は普及して、弊社は試験問題集を提供する多くのネットの一つでございます。NewValidDumpsが提供したのオンライン商品がIT業界では品質の高い学習資料、受験生の必要が満足できるサイトでございます。 NewValidDumpsには一部の問題と解答を無料に提供して差し上げますから、もし私の話を信じないのなら、試用版を使ってみてください。利用してみたら効果があるかどうか自分でよく知っているようになります。 試験問題と解答に関する質問があるなら、当社は直後に解決方法を差し上げます。

Cloudera Certified CCA175 この重要な認証資格をもうすでに手に入れましたか。

NewValidDumpsのCCA175 - CCA Spark and Hadoop Developer Exam基礎訓練教材を購入したら、あなたは一年間の無料アップデートサービスを取得しました。 NewValidDumpsのCCA175 全真模擬試験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CCA175 全真模擬試験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。

もちろんありますよ。NewValidDumpsの問題集を利用することは正にその最良の方法です。NewValidDumpsはあなたが必要とするすべてのCCA175基礎訓練参考資料を持っていますから、きっとあなたのニーズを満たすことができます。

Cloudera CCA175基礎訓練 - 試験に失敗したら、全額で返金する承諾があります。

ClouderaのCCA175基礎訓練試験は大変です。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ClouderaのCCA175基礎訓練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ClouderaのCCA175基礎訓練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、ClouderaのCCA175基礎訓練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ClouderaのCCA175基礎訓練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。

CCA175 PDF DEMO:

QUESTION NO: 1
CORRECT TEXT
Problem Scenario 46 : You have been given belwo list in scala (name,sex,cost) for each work done.
List( ("Deeapak" , "male", 4000), ("Deepak" , "male", 2000), ("Deepika" , "female",
2000),("Deepak" , "female", 2000), ("Deepak" , "male", 1000) , ("Neeta" , "female", 2000))
Now write a Spark program to load this list as an RDD and do the sum of cost for combination of name and sex (as key)
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create an RDD out of this list
val rdd = sc.parallelize(List( ("Deeapak" , "male", 4000}, ("Deepak" , "male", 2000),
("Deepika" , "female", 2000),("Deepak" , "female", 2000), ("Deepak" , "male", 1000} ,
("Neeta" , "female", 2000}}}
Step 2 : Convert this RDD in pair RDD
val byKey = rdd.map({case (name,sex,cost) => (name,sex)->cost})
Step 3 : Now group by Key
val byKeyGrouped = byKey.groupByKey
Step 4 : Nowsum the cost for each group
val result = byKeyGrouped.map{case ((id1,id2),values) => (id1,id2,values.sum)}
Step 5 : Save the results result.repartition(1).saveAsTextFile("spark12/result.txt")

QUESTION NO: 2
CORRECT TEXT
Problem Scenario 40 : You have been given sample data as below in a file called spark15/file1.txt
3070811,1963,1096,,"US","CA",,1,
3022811,1963,1096,,"US","CA",,1,56
3033811,1963,1096,,"US","CA",,1,23
Below is the code snippet to process this tile.
val field= sc.textFile("spark15/f ilel.txt")
val mapper = field.map(x=> A)
mapper.map(x => x.map(x=> {B})).collect
Please fill in A and B so it can generate below final output
Array(Array(3070811,1963,109G, 0, "US", "CA", 0,1, 0)
,Array(3022811,1963,1096, 0, "US", "CA", 0,1, 56)
,Array(3033811,1963,1096, 0, "US", "CA", 0,1, 23)
)
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
A. x.split(","-1)
B. if (x. isEmpty) 0 else x

QUESTION NO: 3
CORRECT TEXT
Problem Scenario 89 : You have been given below patient data in csv format, patientID,name,dateOfBirth,lastVisitDate
1001,Ah Teck,1991-12-31,2012-01-20
1002,Kumar,2011-10-29,2012-09-20
1003,Ali,2011-01-30,2012-10-21
Accomplish following activities.
1 . Find all the patients whose lastVisitDate between current time and '2012-09-15'
2 . Find all the patients who born in 2011
3 . Find all the patients age
4 . List patients whose last visited more than 60 days ago
5 . Select patients 18 years old or younger
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1:
hdfs dfs -mkdir sparksql3
hdfs dfs -put patients.csv sparksql3/
Step 2 : Now in spark shell
// SQLContext entry point for working with structured data
val sqlContext = neworg.apache.spark.sql.SQLContext(sc)
// this is used to implicitly convert an RDD to a DataFrame.
import sqlContext.impIicits._
// Import Spark SQL data types and Row.
import org.apache.spark.sql._
// load the data into a new RDD
val patients = sc.textFilef'sparksqIS/patients.csv")
// Return the first element in this RDD
patients.first()
//define the schema using a case class
case class Patient(patientid: Integer, name: String, dateOfBirth:String , lastVisitDate:
String)
// create an RDD of Product objects
val patRDD = patients.map(_.split(M,M)).map(p => Patient(p(0).tolnt,p(1),p(2),p(3))) patRDD.first() patRDD.count(}
// change RDD of Product objects to a DataFrame val patDF = patRDD.toDF()
// register the DataFrame as a temp table patDF.registerTempTable("patients"}
// Select data from table
val results = sqlContext.sql(......SELECT* FROM patients '.....)
// display dataframe in a tabular format
results.show()
//Find all the patients whose lastVisitDate between current time and '2012-09-15' val results = sqlContext.sql(......SELECT * FROM patients WHERE
TO_DATE(CAST(UNIX_TIMESTAMP(lastVisitDate, 'yyyy-MM-dd') AS TIMESTAMP))
BETWEEN '2012-09-15' AND current_timestamp() ORDER BY lastVisitDate......) results.showQ
/.Find all the patients who born in 2011
val results = sqlContext.sql(......SELECT * FROM patients WHERE
YEAR(TO_DATE(CAST(UNIXJTlMESTAMP(dateOfBirth, 'yyyy-MM-dd') AS
TIMESTAMP))) = 2011 ......)
results. show()
//Find all the patients age
val results = sqlContext.sql(......SELECT name, dateOfBirth, datediff(current_date(),
TO_DATE(CAST(UNIX_TIMESTAMP(dateOfBirth, 'yyyy-MM-dd') AS TlMESTAMP}}}/365
AS age
FROM patients
Mini >
results.show()
//List patients whose last visited more than 60 days ago
-- List patients whose last visited more than 60 days ago
val results = sqlContext.sql(......SELECT name, lastVisitDate FROM patients WHERE datediff(current_date(), TO_DATE(CAST(UNIX_TIMESTAMP[lastVisitDate, 'yyyy-MM-dd')
AS T1MESTAMP))) > 60......);
results. showQ;
-- Select patients 18 years old or younger
SELECT' FROM patients WHERE TO_DATE(CAST(UNIXJTlMESTAMP(dateOfBirth,
'yyyy-MM-dd') AS TIMESTAMP}) > DATE_SUB(current_date(),INTERVAL 18 YEAR); val results = sqlContext.sql(......SELECT' FROM patients WHERE
TO_DATE(CAST(UNIX_TIMESTAMP(dateOfBirth, 'yyyy-MM--dd') AS TIMESTAMP)) >
DATE_SUB(current_date(), T8*365)......);
results. showQ;
val results = sqlContext.sql(......SELECT DATE_SUB(current_date(), 18*365) FROM patients......); results.show();

QUESTION NO: 4
CORRECT TEXT
Problem Scenario 35 : You have been given a file named spark7/EmployeeName.csv
(id,name).
EmployeeName.csv
E01,Lokesh
E02,Bhupesh
E03,Amit
E04,Ratan
E05,Dinesh
E06,Pavan
E07,Tejas
E08,Sheela
E09,Kumar
E10,Venkat
1. Load this file from hdfs and sort it by name and save it back as (id,name) in results directory.
However, make sure while saving it should be able to write In a single file.
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution:
Step 1 : Create file in hdfs (We will do using Hue). However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : Load EmployeeName.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark7/EmployeeName.csv")
val namePairRDD = name.map(x=> (x.split(",")(0),x.split(",")(1)))
Step 3 : Now swap namePairRDD RDD.
val swapped = namePairRDD.map(item => item.swap)
step 4: Now sort the rdd by key.
val sortedOutput = swapped.sortByKey()
Step 5 : Now swap the result back
val swappedBack = sortedOutput.map(item => item.swap}
Step 6 : Save the output as a Text file and output must be written in a single file.
swappedBack. repartition(1).saveAsTextFile("spark7/result.txt")

QUESTION NO: 5
CORRECT TEXT
Problem Scenario 96 : Your spark application required extra Java options as below. -
XX:+PrintGCDetails-XX:+PrintGCTimeStamps
Please replace the XXX values correctly
./bin/spark-submit --name "My app" --master local[4] --conf spark.eventLog.enabled=talse -
-conf XXX hadoopexam.jar
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution
XXX: Mspark.executoi\extraJavaOptions=-XX:+PrintGCDetails -XX:+PrintGCTimeStamps"
Notes: ./bin/spark-submit \
--class <maln-class>
--master <master-url> \
--deploy-mode <deploy-mode> \
-conf <key>=<value> \
# other options
< application-jar> \
[application-arguments]
Here, conf is used to pass the Spark related contigs which are required for the application to run like any specific property(executor memory) or if you want to override the default property which is set in Spark-default.conf.

NewValidDumpsはあなたのClouderaのSAP C_S4CPR_2402試験を準備する間あなたの最もよい友達です。 我々NewValidDumpsはClouderaのSalesforce ANC-301試験問題集をリリースする以降、多くのお客様の好評を博したのは弊社にとって、大変な名誉なことです。 EMC D-NWG-FN-23 - 英語版と日本語版の内容は同じですけど、言語だけ違いがあります。 たとえば、ベストセラーのCloudera　EMC D-GAI-F-01問題集は過去のデータを分析して作成ます。 Amazon SOA-C02 - あるいは、他の科目の試験を変えていいです。

Updated: May 28, 2022