最新なCloudera CCA175問題集（96題）、真実試験の問題を全部にカバー！

質問 1：
CORRECT TEXT
Problem Scenario 15 : 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 activities.
1. In mysql departments table please insert following record. Insert into departments values(9999, '"Data Science"1);
2. Now there is a downstream system which will process dumps of this file. However, system is designed the way that it can process only files if fields are enlcosed in(') single quote and separate of the field should be (-} and line needs to be terminated by : (colon).
3. If data itself contains the " (double quote } than it should be escaped by \.
4. Please import the departments table in a directory called departments_enclosedby and file should be able to process by downstream system.
正解：
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Connect to mysql database.
mysql --user=retail_dba -password=cloudera
show databases; use retail_db; show tables;
Insert record
Insert into departments values(9999, '"Data Science"');
select" from departments;
Step 2 : Import data as per requirement.
sqoop import \
-connect jdbc:mysql;//quickstart:3306/retail_db \
~ username=retail_dba \
--password=cloudera \
-table departments \
-target-dir /user/cloudera/departments_enclosedby \
-enclosed-by V -escaped-by \\ -fields-terminated-by--' -lines-terminated-by :
Step 3 : Check the result.
hdfs dfs -cat/user/cloudera/departments_enclosedby/part"

質問 2：
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)
)
正解：
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
A. x.split(","-1)
B. if (x. isEmpty) 0 else x

質問 3：
CORRECT TEXT
Problem Scenario 88 : You have been given below three files
product.csv (Create this file in hdfs)
productID,productCode,name,quantity,price,supplierid
1001,PEN,Pen Red,5000,1.23,501
1002,PEN,Pen Blue,8000,1.25,501
1003,PEN,Pen Black,2000,1.25,501
1004,PEC,Pencil 2B,10000,0.48,502
1005,PEC,Pencil 2H,8000,0.49,502
1006,PEC,Pencil HB,0,9999.99,502
2001,PEC,Pencil 3B,500,0.52,501
2002,PEC,Pencil 4B,200,0.62,501
2003,PEC,Pencil 5B,100,0.73,501
2004,PEC,Pencil 6B,500,0.47,502
supplier.csv
supplierid,name,phone
501,ABC Traders,88881111
502,XYZ Company,88882222
503,QQ Corp,88883333
products_suppliers.csv
productID,supplierID
2001,501
2002,501
2003,501
2004,502
2001,503
Now accomplish all the queries given in solution.
1. It is possible that, same product can be supplied by multiple supplier. Now find each product, its price according to each supplier.
2. Find all the supllier name, who are supplying 'Pencil 3B'
3. Find all the products , which are supplied by ABC Traders.
正解：
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : It is possible that, same product can be supplied by multiple supplier. Now find each product, its price according to each supplier.
val results = sqlContext.sql(......SELECT products.name AS Product Name', price, suppliers.name AS Supplier Name'
FROM products_suppliers
JOIN products ON products_suppliers.productlD = products.productID JOIN suppliers ON products_suppliers.supplierlD = suppliers.supplierlD null t results.show()
Step 2 : Find all the supllier name, who are supplying 'Pencil 3B'
val results = sqlContext.sql(......SELECT p.name AS 'Product Name", s.name AS "Supplier
Name'
FROM products_suppliers AS ps
JOIN products AS p ON ps.productID = p.productID
JOIN suppliers AS s ON ps.supplierlD = s.supplierlD
WHERE p.name = 'Pencil 3B"",M )
results.show()
Step 3 : Find all the products , which are supplied by ABC Traders.
val results = sqlContext.sql(......SELECT p.name AS 'Product Name", s.name AS "Supplier
Name'
FROM products AS p, products_suppliers AS ps, suppliers AS s WHERE p.productID = ps.productID AND ps.supplierlD = s.supplierlD
AND s.name = 'ABC Traders".....)
results. show()

質問 4：
CORRECT TEXT
Problem Scenario 76 : 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
Columns of order table : (orderid , order_date , ordercustomerid, order_status}
.....
Please accomplish following activities.
1 . Copy "retail_db.orders" table to hdfs in a directory p91_orders.
2 . Once data is copied to hdfs, using pyspark calculate the number of order for each status.
3 . Use all the following methods to calculate the number of order for each status. (You need to know all these functions and its behavior for real exam)
- countByKey()
-groupByKey()
- reduceByKey()
-aggregateByKey()
- combineByKey()
正解：
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Import Single table
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail dba - password=cloudera -table=orders --target-dir=p91_orders
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 p91_orders/part-m-00000
Step 3: countByKey #Number of orders by status allOrders = sc.textFile("p91_orders")
#Generate key and value pairs (key is order status and vale as an empty string keyValue = aIIOrders.map(lambda line: (line.split(",")[3], ""))
#Using countByKey, aggregate data based on status as a key
output=keyValue.countByKey()Jtems()
for line in output: print(line)
Step 4 : groupByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split)",")[3], 1))
#Using countByKey, aggregate data based on status as a key output=
keyValue.groupByKey().map(lambda kv: (kv[0], sum(kv[1]}}}
tor line in output.collect(): print(line}
Step 5 : reduceByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(","}[3], 1))
#Using countByKey, aggregate data based on status as a key output=
keyValue.reduceByKey(lambda a, b: a + b)
tor line in output.collect(): print(line}
Step 6: aggregateByKey
#Generate key and value pairs (key is order status and vale as an one keyValue = allOrders.map(lambda line: (line.split(",")[3], line}} output=keyValue.aggregateByKey(0, lambda a, b: a+1, lambda a, b: a+b} for line in output.collect(): print(line}
Step 7 : combineByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(",")[3], line))
output=keyValue.combineByKey(lambda value: 1, lambda ace, value: acc+1, lambda ace, value: acc+value) tor line in output.collect(): print(line)
#Watch Spark Professional Training provided by www.ABCTECH.com to understand more on each above functions. (These are very important functions for real exam)

質問 5：
CORRECT TEXT
Problem Scenario 84 : In Continuation of previous question, please accomplish following activities.
1. Select all the products which has product code as null
2. Select all the products, whose name starts with Pen and results should be order by Price descending order.
3. Select all the products, whose name starts with Pen and results should be order by
Price descending order and quantity ascending order.
4. Select top 2 products by price
正解：
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()

質問 6：
CORRECT TEXT
Problem Scenario 78 : 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
Columns of order table : (orderid , order_date , order_customer_id, order_status)
Columns of ordeMtems table : (order_item_td , order_item_order_id ,
order_item_product_id,
order_item_quantity,order_item_subtotal,order_item_product_price)
Please accomplish following activities.
1. Copy "retail_db.orders" and "retail_db.order_items" table to hdfs in respective directory p92_orders and p92_order_items .
2. Join these data using order_id in Spark and Python
3. Calculate total revenue perday and per customer
4. Calculate maximum revenue customer
正解：
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Import Single table .
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=orders --target-dir=p92_orders -m 1 sqoop import -connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba - password=cloudera -table=order_items --target-dir=p92_order_orderitems --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 p92_orders/part-m-00000 hadoop fs -cat p92 orderitems/part-m-00000
Step 3 : Load these above two directory as RDD using Spark and Python (Open pyspark terminal and do following). orders = sc.textFile(Mp92_orders") orderitems = sc.textFile("p92_order_items")
Step 4 : Convert RDD into key value as (orderjd as a key and rest of the values as a value)
#First value is orderjd
orders Key Value = orders.map(lambda line: (int(line.split(",")[0]), line))
#Second value as an Orderjd
orderltemsKeyValue = orderltems.map(lambda line: (int(line.split(",")[1]), line))
Step 5 : Join both the RDD using orderjd
joinedData = orderltemsKeyValue.join(ordersKeyValue)
#print the joined data
for line in joinedData.collect():
print(line)
#Format of joinedData as below.
#[Orderld, 'All columns from orderltemsKeyValue', 'All columns from ordersKeyValue'] ordersPerDatePerCustomer = joinedData.map(lambda line: ((line[1][1].split(",")[1], line[1][1].split(",M)[2]), float(line[1][0].split(",")[4]))) amountCollectedPerDayPerCustomer = ordersPerDatePerCustomer.reduceByKey(lambda runningSum, amount: runningSum + amount}
#(Out record format will be ((date,customer_id), totalAmount} for line in amountCollectedPerDayPerCustomer.collect(): print(line)
#now change the format of record as (date,(customer_id,total_amount))
revenuePerDatePerCustomerRDD = amountCollectedPerDayPerCustomer.map(lambda threeElementTuple: (threeElementTuple[0][0],
(threeElementTuple[0][1],threeElementTuple[1])))
for line in revenuePerDatePerCustomerRDD.collect():
print(line)
#Calculate maximum amount collected by a customer for each day
perDateMaxAmountCollectedByCustomer =
revenuePerDatePerCustomerRDD.reduceByKey(lambda runningAmountTuple,
newAmountTuple: (runningAmountTuple if runningAmountTuple[1] >=
newAmountTuple[1] else newAmountTuple})
for line in perDateMaxAmountCollectedByCustomer\sortByKey().collect(): print(line)

質問 7：
CORRECT TEXT
Problem Scenario 81 : You have been given MySQL DB with following details. You have been given following product.csv file product.csv productID,productCode,name,quantity,price
1001,PEN,Pen Red,5000,1.23
1002,PEN,Pen Blue,8000,1.25
1003,PEN,Pen Black,2000,1.25
1004,PEC,Pencil 2B,10000,0.48
1005,PEC,Pencil 2H,8000,0.49
1006,PEC,Pencil HB,0,9999.99
Now accomplish following activities.
1 . Create a Hive ORC table using SparkSql
2 . Load this data in Hive table.
3 . Create a Hive parquet table using SparkSQL and load data in it.
正解：
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create this tile in HDFS under following directory (Without header}
/user/cloudera/he/exam/task1/productcsv
Step 2 : Now using Spark-shell read the file as RDD
// load the data into a new RDD
val products = sc.textFile("/user/cloudera/he/exam/task1/product.csv")
// Return the first element in this RDD
prod u cts.fi rst()
Step 3 : Now define the schema using a case class
case class Product(productid: Integer, code: String, name: String, quantity:lnteger, price:
Float)
Step 4 : create an RDD of Product objects
val prdRDD = products.map(_.split(",")).map(p =>
Product(p(0).tolnt,p(1),p(2),p(3}.tolnt,p(4}.toFloat))
prdRDD.first()
prdRDD.count()
Step 5 : Now create data frame val prdDF = prdRDD.toDF()
Step 6 : Now store data in hive warehouse directory. (However, table will not be created } import org.apache.spark.sql.SaveMode prdDF.write.mode(SaveMode.Overwrite).format("orc").saveAsTable("product_orc_table") step 7: Now create table using data stored in warehouse directory. With the help of hive.
hive
show tables
CREATE EXTERNAL TABLE products (productid int,code string,name string .quantity int, price float}
STORED AS ore
LOCATION 7user/hive/warehouse/product_orc_table';
Step 8 : Now create a parquet table
import org.apache.spark.sql.SaveMode
prdDF.write.mode(SaveMode.Overwrite).format("parquet").saveAsTable("product_parquet_ table")
Step 9 : Now create table using this
CREATE EXTERNAL TABLE products_parquet (productid int,code string,name string
.quantity int, price float}
STORED AS parquet
LOCATION 7user/hive/warehouse/product_parquet_table';
Step 10 : Check data has been loaded or not.
Select * from products;
Select * from products_parquet;