Our CCA175 Soft Simulations simulating exam is perfect for they come a long way on their quality. On one hand, we have engaged in this career for over ten years and have become the leader in this market. On the other hand, we never stop developing our CCA175 Soft Simulations study guide. Most people are nervous and anxious to take part in the CCA175 Soft Simulations exam for the first time. Then it is easy for them to make mistakes. As long as you pay for our CCA175 Soft Simulations study guide successfully, then you will receive it quickly.
Cloudera Certified CCA175 Many people think this is incredible.
Choose CCA175 - CCA Spark and Hadoop Developer Exam Soft Simulations test guide to get you closer to success. The comprehensive coverage would be beneficial for you to pass the exam. Only need to spend about 20-30 hours practicing our CCA175 Latest Exam Questions Answers study files can you be fully prepared for the exam.
It is common in modern society that many people who are more knowledgeable and capable than others finally lost some good opportunities for development because they didn’t obtain the CCA175 Soft Simulations certification. The prerequisite for obtaining the CCA175 Soft Simulations certification is to pass the exam, but not everyone has the ability to pass it at one time. But our CCA175 Soft Simulations exam questions will help you pass the exam by just one go for we have the pass rate high as 98% to 100%.
So our Cloudera CCA175 Soft Simulations study questions are their best choice.
With the rise of internet and the advent of knowledge age, mastering knowledge about computer is of great importance. This CCA175 Soft Simulations exam is your excellent chance to master more useful knowledge of it. Up to now, No one has questioned the quality of our CCA175 Soft Simulations training materials, for their passing rate has reached up to 98 to 100 percent. If you make up your mind of our CCA175 Soft Simulations exam questions after browsing the free demos, we will staunchly support your review and give you a comfortable and efficient purchase experience this time.
And our online test engine and the windows software of the CCA175 Soft Simulations guide materials are designed more carefully. During our researching and developing, we always obey the principles of conciseness and exquisiteness.
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 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: 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.
Compared with other materials available on the market, the main feature of SAP C-THR70-2404 exam materials doesn’t like other materials simply list knowledge points. Omgzlook can help you pass Cloudera certification ISACA CISA exam and can also help you in the future about your work. First of all, the PDF version SAP C-S4FCF-2023 certification materials are easy to carry and have no restrictions. If you purchase the training materials we provide, you can pass Cloudera certification Snowflake DEA-C01 exam successfully. Fortinet NSE7_LED-7.0 - Sometimes, we will receive some good suggestions from our users.
Updated: May 28, 2022