CCA175 試験問題を無料オンラインアクセス

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Problem Scenario 36 : You have been given a file named spark8/data.csv (type,name).
data.csv
1 ,Lokesh
2 ,Bhupesh
2 ,Amit
2 ,Ratan
2 ,Dinesh
1 ,Pavan
1 ,Tejas
2 ,Sheela
1 ,Kumar
1 ,Venkat
1. Load this file from hdfs and save it back as (id, (all names of same type)) in results directory. However, make sure while saving it should be

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Problem Scenario 71 :
Write down a Spark script using Python,
In which it read a file "Content.txt" (On hdfs) with following content.
After that split each row as (key, value), where key is first word in line and entire line as value.
Filter out the empty lines.
And save this key value in "problem86" as Sequence file(On hdfs)
Part 2 : Save as sequence file , where key as null and entire line as value. Read back the stored sequence files.
Content.txt
Hello this is ABCTECH.com
This is XYZTECH.com
Apache Spark Training
This is Spark Learning Session
Spark is faster than MapReduce

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Problem Scenario 18 : 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
Now accomplish following activities.
1. Create mysql table as below.
mysql --user=retail_dba -password=cloudera
use retail_db
CREATE TABLE IF NOT EXISTS departments_hive02(id int, department_name
varchar(45), avg_salary int);
show tables;
2. Now export data from hive table departments_hive01 in departments_hive02. While exporting, please note following. wherever there is a empty string it should be loaded as a null value in mysql.
wherever there is -999 value for int field, it should be created as null value.

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Problem Scenario 67 : You have been given below code snippet.
lines = sc.parallelize(['lts fun to have fun,','but you have to know how.'])
M = lines.map( lambda x: x.replace(',7 ').replace('.',' 'J.replaceC-V ').lower()) r2 = r1.flatMap(lambda x: x.split()) r3 = r2.map(lambda x: (x, 1)) operation1
r5 = r4.map(lambda x:(x[1],x[0]))
r6 = r5.sortByKey(ascending=False)
r6.take(20)
Write a correct code snippet for operationl which will produce desired output, shown below.
[(2, 'fun'), (2, 'to'), (2, 'have'), (1, its'), (1, 'know1), (1, 'how1), (1, 'you'), (1, 'but')]

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Problem Scenario 47 : You have been given below code snippet, with intermediate output.
val z = sc.parallelize(List(1,2,3,4,5,6), 2)
// lets first print out the contents of the RDD with partition labels
def myfunc(index: Int, iter: lterator[(lnt)]): lterator[String] = {
iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator
}
//In each run , output could be different, while solving problem assume belowm output only.
z.mapPartitionsWithlndex(myfunc).collect
res28: Array[String] = Array([partlD:0, val: 1], [partlD:0, val: 2], [partlD:0, val: 3], [partlD:1, val: 4], [partlD:1, val: S], [partlD:1, val: 6])
Now apply aggregate method on RDD z , with two reduce function , first will select max value in each partition and second will add all the maximum values from all partitions.
Initialize the aggregate with value 5. hence expected output will be 16.