きみはClouderaのCCA175合格内容認定テストに合格するためにたくさんのルートを選択肢があります。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。あなたの全部な需要を満たすためにいつも頑張ります。 なぜ我々のClouderaのCCA175合格内容ソフトに自信があるかと聞かれたら、まずは我々NewValidDumpsの豊富な経験があるチームです、次は弊社の商品を利用してClouderaのCCA175合格内容試験に合格する多くのお客様です。ClouderaのCCA175合格内容試験は国際的に認められてあなたはこの認証がほしいですか。 NewValidDumpsの専門家チームがClouderaのCCA175合格内容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。

Cloudera Certified CCA175 NewValidDumpsを選択したら、成功をとりましょう。

自分の能力を証明するために、CCA175 - CCA Spark and Hadoop Developer Exam合格内容試験に合格するのは不可欠なことです。 NewValidDumpsの勉強資料を手に入れたら、指示に従えば CCA175 受験記認定試験に受かることはたやすくなります。受験生の皆様にもっと多くの助けを差し上げるために、NewValidDumps のClouderaのCCA175 受験記トレーニング資料はインターネットであなたの緊張を解消することができます。

我々NewValidDumpsは一番行き届いたアフタサービスを提供します。Cloudera　CCA175合格内容試験問題集を購買してから、一年間の無料更新を楽しみにしています。あなたにCloudera　CCA175合格内容試験に関する最新かつ最完備の資料を勉強させ、試験に合格させることだと信じます。

Cloudera CCA175合格内容 - 学歴はどんなに高くても実力を代表できません。

NewValidDumpsはIT試験問題集を提供するウエブダイトで、ここによく分かります。最もよくて最新で資料を提供いたします。こうして、君は安心で試験の準備を行ってください。弊社の資料を使って、１００％に合格を保証いたします。もし合格しないと、われは全額で返金いたします。NewValidDumpsはずっと君のために最も正確なClouderaのCCA175合格内容「CCA Spark and Hadoop Developer Exam」試験に関する資料を提供して、君が安心に選択することができます。君はオンラインで無料な練習問題をダウンロードできて、１００％で試験に合格しましょう。

そのためにClouderaのCCA175合格内容認定試験に受かる必要があります。適当なトレーニング資料を選んだらこの試験はそんなに難しくなくなります。

CCA175 PDF DEMO:

QUESTION NO: 1
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: 2
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: 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 13 : You have been given following mysql database details as well as other info.
user=retail_dba
password=cloudera
database=retail_db
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following.
1. Create a table in retailedb with following definition.
CREATE table departments_export (department_id int(11), department_name varchar(45), created_date T1MESTAMP DEFAULT NOWQ);
2. Now import the data from following directory into departments_export table,
/user/cloudera/departments new
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Login to musql db
mysql --user=retail_dba -password=cloudera
show databases; use retail_db; show tables;
step 2 : Create a table as given in problem statement.
CREATE table departments_export (departmentjd int(11), department_name varchar(45), created_date T1MESTAMP DEFAULT NOW()); show tables;
Step 3 : Export data from /user/cloudera/departmentsnew to new table departments_export sqoop export -connect jdbc:mysql://quickstart:3306/retail_db \
-username retaildba \
--password cloudera \
--table departments_export \
-export-dir /user/cloudera/departments_new \
-batch
Step 4 : Now check the export is correctly done or not. mysql -user*retail_dba - password=cloudera show databases; use retail _db;
show tables;
select' from departments_export;

QUESTION NO: 5
Select top 2 products by price
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Select all the products which has product code as null
val results = sqlContext.sql(......SELECT' FROM products WHERE code IS NULL......) results. showQ val results = sqlContext.sql(......SELECT * FROM products WHERE code = NULL ",,M ) results.showQ
Step 2 : Select all the products , whose name starts with Pen and results should be order by Price descending order. val results = sqlContext.sql(......SELECT * FROM products
WHERE name LIKE 'Pen %' ORDER BY price DESC......)
results. showQ
Step 3 : Select all the products , whose name starts with Pen and results should be order by Price descending order and quantity ascending order. val results = sqlContext.sql('.....SELECT * FROM products WHERE name LIKE 'Pen %' ORDER BY price DESC, quantity......) results. showQ
Step 4 : Select top 2 products by price
val results = sqlContext.sql(......SELECT' FROM products ORDER BY price desc
LIMIT2......}
results. show()
4. CORRECT TEXT
Problem Scenario 4: You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.categories
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following activities.
Import Single table categories (Subset data} to hive managed table , where category_id between 1 and 22
Answer:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Import Single table (Subset data)
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=categories -where "\'category_id\' between 1 and 22" --hive- import --m 1
Note: Here the ' is the same you find on ~ key
This command will create a managed table and content will be created in the following directory.
/user/hive/warehouse/categories
Step 2 : Check whether table is created or not (In Hive)
show tables;
select * from categories;

Cloudera SAP C-S4CS-2402「CCA Spark and Hadoop Developer Exam」認証試験に合格することが簡単ではなくて、Cloudera SAP C-S4CS-2402証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。 Snowflake ARA-C01 - NewValidDumpsの練習資料を利用すれば、あなたはこの資料の特別と素晴らしさをはっきり感じることができます。 Scrum SPS - 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。 Cisco 300-810 - 心配する必要はないです。 NewValidDumpsは実際の環境で本格的なClouderaのLpi 201-450「CCA Spark and Hadoop Developer Exam」の試験の準備過程を提供しています。

Updated: May 28, 2022