Free CCA175 Exam braindumps download (CCA175 exam dumps Free)

QUESTION: 26 CORRECT TEXT

Problem Scenario 75 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.orders
table=retail_db.order_items
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following activities.

1. Copy "retail_db.order_items" table to hdfs in respective directory p90_order_items .
2. Do the summation of entire revenue in this table using pyspark.
3. Find the maximum and minimum revenue as well.
4. Calculate average revenue
Columns of ordeMtems table : (order_item_id , order_item_order_id ,
order_item_product_id, order_item_quantity,order_item_subtotal,order_
item_subtotal,order_item_product_price)

See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :
Step 1: Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=order_items --target -dir=p90 ordeMtems --m 1
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the
MapReduce framework to copy data from RDBMS to hdfs
Step 2: Read the data from one of the partition, created using above command. hadoop fs -cat p90_order_items/part-m-00000
Step 3: In pyspark, get the total revenue across all days and orders. entire TableRDD = sc.textFile("p90_order_items")
#Cast string to float
extractedRevenueColumn = entireTableRDD.map(lambda line: float(line.split(", ")[4]))
Step 4: Verify extracted data
for revenue in extractedRevenueColumn.collect():
print revenue
#use reduce'function to sum a single column vale
totalRevenue = extractedRevenueColumn.reduce(lambda a, b: a + b)
Step 5: Calculate the maximum revenue
maximumRevenue = extractedRevenueColumn.reduce(lambda a, b: (a if a>=b else b))
Step 6: Calculate the minimum revenue
minimumRevenue = extractedRevenueColumn.reduce(lambda a, b: (a if a<=b else b))
Step 7: Caclculate average revenue
count=extractedRevenueColumn.count()
averageRev=totalRevenue/count

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QUESTION: 27 CORRECT TEXT

Problem Scenario 69 : Write down a Spark Application using Python,
In which it read a file "Content.txt" (On hdfs) with following content.
And filter out the word which is less than 2 characters and ignore all empty lines.
Once doen store the filtered data in a directory called "problem84" (On hdfs)
Content.txt

Hello this is ABCTECH.com
This is ABYTECH.com
Apache Spark Training
This is Spark Learning Session
Spark is faster than MapReduce

See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :
Step 1: Create an application with following code and store it in problem84.py
# Import SparkContext and SparkConf
from pyspark import SparkContext, SparkConf
# Create configuration object and set App name
conf = SparkConf().setAppName("CCA 175 Problem 84") sc = sparkContext(conf=conf)
#load data from hdfs
contentRDD = sc.textFile(MContent.txt")
#filter out non-empty lines
nonemptyjines = contentRDD.filter(lambda x: len(x) > 0)
#Split line based on space
words = nonempty_lines.ffatMap(lambda x: x.split(''}}
#filter out all 2 letter words
finalRDD = words.filter(lambda x: len(x) > 2)
for word in finalRDD.collect():
print(word)
#Save final data finalRDD.saveAsTextFile("problem84M)

Step 2: Submit this application
spark-submit -master yarn problem84.py

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QUESTION: 28 CORRECT TEXT

Problem Scenario 80 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.products
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Columns of products table : (product_id | product_category_id | product_name | product_description | product_price | product_image )
Please accomplish following activities.

1. Copy "retaildb.products" table to hdfs in a directory p93_products
2. Now sort the products data sorted by product price per category, use productcategoryid
colunm to group by category

See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :
Step 1: Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=products --target-dir=p93
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the MapReduce framework to copy data from RDBMS to hdfs
Step 2: Read the data from one of the partition, created using above command, hadoop fs -cat p93_products/part-m-00000
Step 3: Load this directory as RDD using Spark and Python (Open pyspark terminal and do following}. productsRDD = sc.textFile(Mp93_products")
Step 4: Filter empty prices, if exists
#filter out empty prices lines
Nonempty_lines = productsRDD.filter(lambda x: len(x.split(", ")[4]) > 0)
Step 5: Create data set like (categroyld, (id, name, price) mappedRDD = nonempty_lines.map(lambda line: (line.split(", ")[1], (line.split(", ")[0], line.split(", ")[2], float(line.split(", ")[4]))))
tor line in mappedRDD.collect(): print(line)
Step 6: Now groupBy the all records based on categoryld, which a key on mappedRDD it will produce output like (categoryld, iterable of all lines for a key/categoryld) groupByCategroyld = mappedRDD.groupByKey() for line in groupByCategroyld.collect():
print(line)

Step 7: Now sort the data in each category based on price in ascending order.
# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price on which it needs to be sorted. groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue:
tupleValue[2])).take(5)
Step 8: Now sort the data in each category based on price in descending order. # sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price which it needs to be sorted. on groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue:
tupleValue[2] , reverse=True)).take(5)

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QUESTION: 29 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)
)

See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :
A) x.split(", "-1)
B) if (x. isEmpty) 0 else x

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QUESTION: 30 CORRECT TEXT

Problem Scenario 42 : You have been given a file (sparklO/sales.txt), with the content as given in below.
spark10/sales.txt
Department, Designation, costToCompany, State
Sales, Trainee, 12000, UP
Sales, Lead, 32000, AP
Sales, Lead, 32000, LA
Sales, Lead, 32000, TN
Sales, Lead, 32000, AP
Sales, Lead, 32000, TN
Sales, Lead, 32000, LA
Sales, Lead, 32000, LA
Marketing, Associate, 18000, TN
Marketing, Associate, 18000, TN
HR, Manager, 58000, TN
And want to produce the output as a csv with group by Department, Designation, State with additional columns with sum(costToCompany) and TotalEmployeeCountt
Should get result like
Dept, Desg, state, empCount, totalCost
Sales, Lead, AP, 2, 64000
Sales.Lead.LA.3.96000
Sales, Lead, TN, 2, 64000

See the explanation for Step by Step Solution and configuration.

Answer(s): A

Explanation:

Solution :
Step 1: Create a file first using Hue in hdfs.
Step 2: Load tile as an RDD
val rawlines = sc.textFile("spark10/sales.txt")
Step 3: Create a case class, which can represent its column fileds. case class Employee(dep: String, des: String, cost: Double, state: String)
Step 4: Split the data and create RDD of all Employee objects. val employees = rawlines.map(_.split(", ")).map(row=>Employee(row(0), row{1), row{2).toDouble, row{3)))

Step 5: Create a row as we needed. All group by fields as a key and value as a count for each employee as well as its cost, val keyVals = employees.map( em => ((em.dep, em.des, em.state), (1 , em.cost)))
Step 6: Group by all the records using reduceByKey method as we want summation as well. For number of employees and their total cost, val results = keyVals.reduceByKey{ (a, b) => (a._1 + b._1, a._2 + b._2)} // (a.count + b.count, a.cost + b.cost)}
Step 7: Save the results in a text file as below.
results.repartition(1).saveAsTextFile("spark10/group.txt")

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Free CCA175 Exam Braindumps CCA Spark and Hadoop Developer Exam Questions 26–30 (page: 7)

QUESTION: 26 CORRECT TEXT

Explanation:

QUESTION: 27 CORRECT TEXT

Explanation:

QUESTION: 28 CORRECT TEXT

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QUESTION: 29 CORRECT TEXT

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QUESTION: 30 CORRECT TEXT

Explanation:

Free CCA175 Exam Braindumps
CCA Spark and Hadoop Developer Exam Questions 26–30 (page: 7)