Our training materials, including questions and answers, the pass rate can reach 100%. With Omgzlook Cloudera CCA175 Valid Exam Collection Materials exam training materials, you can begin your first step forward. When you get the certification of Cloudera CCA175 Valid Exam Collection Materials exam, the glorious period of your career will start. It is universally acknowledged that time is a key factor in terms of the success of exams. The more time you spend in the preparation for CCA175 Valid Exam Collection Materials learning engine, the higher possibility you will pass the exam. When you are faced with the real exam, you can pass Cloudera CCA175 Valid Exam Collection Materials test easily.
Cloudera Certified CCA175 It is the dumps that you can't help praising it.
Cloudera Certified CCA175 Valid Exam Collection Materials - CCA Spark and Hadoop Developer Exam As far as concerned, the online mode for mobile phone clients has the same function. Are you still searching proper New Dumps CCA175 Free Download exam study materials, or are you annoying of collecting these study materials? As the professional IT exam dumps provider, Omgzlook has offered the complete New Dumps CCA175 Free Download exam materials for you. So you can save your time to have a full preparation of New Dumps CCA175 Free Download exam.
As a matter of fact, since the establishment, we have won wonderful feedback and ceaseless business, continuously working on developing our CCA175 Valid Exam Collection Materials test prep. We have been specializing CCA175 Valid Exam Collection Materials exam dumps many years and have a great deal of long-term old clients, and we would like to be a reliable cooperator on your learning path and in your further development. While you are learning with our CCA175 Valid Exam Collection Materials quiz guide, we hope to help you make out what obstacles you have actually encountered during your approach for CCA175 Valid Exam Collection Materials exam torrent through our PDF version, only in this way can we help you win the CCA175 Valid Exam Collection Materials certification in your first attempt.
Cloudera CCA175 Valid Exam Collection Materials - In fact, our aim is the same with you.
For a long time, high quality is our CCA175 Valid Exam Collection Materials exam questions constantly attract students to participate in the use of important factors, only the guarantee of high quality, to provide students with a better teaching method, and at the same time the CCA175 Valid Exam Collection Materials practice quiz brings more outstanding teaching effect. Our high-quality CCA175 Valid Exam Collection Materials} learning guide help the students know how to choose suitable for their own learning method, our CCA175 Valid Exam Collection Materials study materials are a very good option.
More importantly, it is evident to all that the CCA175 Valid Exam Collection Materials training materials from our company have a high quality, and we can make sure that the quality of our products will be higher than other study materials in the market. If you want to pass the CCA175 Valid Exam Collection Materials exam and get the related certification in the shortest time, choosing the CCA175 Valid Exam Collection Materials training materials from our company will be in the best interests of all people.
CCA175 PDF DEMO:
QUESTION NO: 1
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: 2
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: 3
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: 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
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
All the preparation material reflects latest updates in CheckPoint 156-315.81 certification exam pattern. Salesforce PDX-101 - The trick to the success is simply to be organized, efficient, and to stay positive about it. Microsoft MB-330 - This innovative facility provides you a number of practice questions and answers and highlights the weak points in your learning. Microsoft PL-400-KR - Who will refuse such a wonderful dream? So you must struggle for a better future. APICS CSCP-KR - Our behavior has been strictly ethical and responsible to you, which is trust worthy.
Updated: May 28, 2022