CCA175試験復習赤本「CCA Spark and Hadoop Developer Exam」はClouderaの一つ認証試験として、もしCloudera認証試験に合格してIT業界にとても人気があってので、ますます多くの人がCCA175試験復習赤本試験に申し込んで、CCA175試験復習赤本試験は簡単ではなくて、時間とエネルギーがかかって用意しなければなりません。 試験が更新されているうちに、我々はClouderaのCCA175試験復習赤本試験の資料を更新し続けています。できるだけ100％の通過率を保証使用にしています。 しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。

Cloudera Certified CCA175 自分の幸せは自分で作るものだと思われます。

ClouderaのCCA175 - CCA Spark and Hadoop Developer Exam試験復習赤本認定試験は競争が激しい今のIT業界中でいよいよ人気があって、受験者が増え一方で難度が低くなくて結局専門知識と情報技術能力の要求が高い試験なので、普通の人がCloudera認証試験に合格するのが必要な時間とエネルギーをかからなければなりません。 あなたは弊社の高品質Cloudera　CCA175 復習問題集試験資料を利用して、一回に試験に合格します。NewValidDumpsのCloudera　CCA175 復習問題集問題集は専門家たちが数年間で過去のデータから分析して作成されて、試験にカバーする範囲は広くて、受験生の皆様のお金と時間を節約します。

IT業界の一员として、君はまだIT認証試験を悩んでいますか？認証試験はITの専門知識を主なテストとして別に初めてIT関連の認証試験に参加する受験生にとってはとても難しいとみされます。良い対応性の訓練が必要で、NewValidDumps の問題集をお勧めます。

Cloudera CCA175試験復習赤本 - 例外がないです。

ClouderaのCCA175試験復習赤本の認定試験に合格すれば、就職機会が多くなります。この試験に合格すれば君の専門知識がとても強いを証明し得ます。ClouderaのCCA175試験復習赤本の認定試験は君の実力を考察するテストでございます。

NewValidDumpsが提供した製品は真実なもので、しかも価格は非常に合理的です。NewValidDumpsの製品を選んだら、あなたがもっと充分の時間で試験に準備できるように、当社は一年間の無料更新サービスを提供します。

CCA175 PDF DEMO:

QUESTION NO: 1
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: 2
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: 3
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: 4
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.

QUESTION NO: 5
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")

Salesforce CRT-403 - NewValidDumpsは君のために良い訓練ツールを提供し、君のCloudera認証試に高品質の参考資料を提供しいたします。 Nutanix NCP-MCI-6.5 - NewValidDumpsを選んだら成功を選ぶということです。 NewValidDumpsの専門家チームがClouderaのAmazon SOA-C02認証試験に対して最新の短期有効なトレーニングプログラムを研究しました。 CheckPoint 156-536 - 困難に直面するとき、勇敢な人だけはのんびりできます。 NewValidDumpsのClouderaのSAP C_TS462_2022認証試験について最新な研究を完成いたしました。

Updated: May 28, 2022