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NEW QUESTION: 1

A. Option A
B. Option B
C. Option D
D. Option C
Answer: A,D

NEW QUESTION: 2
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()
Answer:
Explanation:
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)

NEW QUESTION: 3
DRAG DROP
You are developing a SQL Server Analysis Services (S5AS) cube. The cube consists of a single measure group.
The measure group consists of one partition that uses MOLAP.
The proactive caching policy has the following requirements:
- The cache must be updated when data is changed in the table named tbIOrders. - Changes must be notified through the use of the XML for Analysis (XMLA) NotifyTableChange command.
You need to configure the proactive caching policy to meet the requirements.
Which three actions should you perform in sequence? (To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.)

Answer:
Explanation: