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NEW QUESTION: 1
What three major tasks are performed by a Designated Intermediate System in an ISIS pseudonode environment? (Choose three.)
A. election of the pseudonode
B. flooding LSPs over the LAN
C. creating the pseudonode LSP
D. maintaining pseudonode link-state information
E. updating the pseudonode LSP
Answer: B,C,E
NEW QUESTION: 2
You are designing a service layer endpoint named EndPoint1 that will read more than one million rows from a database named DB1, and then update several rows in multiple tables in a database named DB2.
You need to identify a data access strategy that meets the following requirements:
* Uses the OData protocol to retrieve data from EndPoint1
* Creates a strongly typed object based on the table in BD2
* Retrieves data from DB1 as quickly as possible, while minimizing memory use on the application server What should you identify for each requirement? To answer, drag the appropriate data access strategies to the correct requirements. Each data access strategy may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Answer:
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/dotnet/framework/data/wcf/wcf-data-services-overview
https://docs.microsoft.com/en-us/dotnet/framework/data/adonet/populating-a-dataset-from-a-dataadapter
https://docs.microsoft.com/en-us/dotnet/framework/data/adonet/retrieving-data-using-a-datareader
NEW QUESTION: 3
조직은 전 세계 사용자에게 서비스를 제공하는 웹 응용 프로그램을 운영합니다. 애플리케이션은 Application Load Balancer 뒤의 Amazon EC2 인스턴스에서 실행됩니다. 로드 밸런서 앞에 Amazon CloudFront 배포가 있으며 조직은 AWS WAF를 사용합니다. 응용 프로그램이 현재 대량 공격을 겪고있어 공격자가 인기있는 모바일 게임에서 버그를 악용하고 있습니다.
응용 프로그램에 User-Agent가 다음 문자열로 설정된 전 세계의 HTTP 요청이 쇄도하고 있습니다. Mozilla / 5.0 (호환 가능; ExampleCorp; ExampleGame / 1.22; Mobile / 1.0) 다음으로 인한 공격 차단에 적용 할 수있는 완화 방법 합법적 인 요청을 계속 처리하면서이 버그가 발생합니까?
A. AWS WAF에서 요금 기반 규칙을 생성하여 웹 애플리케이션이 서비스하는 총 요청 수를 제한하십시오.
B. 대부분의 지역에서 애플리케이션에 액세스하지 못하도록 CloudFront 배포에 대한 지리적 제한을 만듭니다.
C. AWS WAF에서 IP 기반 블랙리스트를 생성하여 User-Agent 헤더에 ExampleGame / 1.22가 포함 된 요청에서 발생하는 IP 주소를 차단하십시오.
D. User-Agent 헤더에 ExampleGame / 1.22가 있는지 여부를 기반으로 요청을 차단하는 조건으로 AWS WAF 규칙에서 규칙을 생성합니다.
Answer: D
NEW QUESTION: 4
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
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=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)
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