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NEW QUESTION: 1
Chaincode Services uses Docker to host (deploy) the chaincode without relying on any virtual machine or computer language.
What would be the main reason or best reason that Hyperledger chose containers over virtual machines?
A. Docker provides a secured, lightweight method to sandbox chaincode execution that is "locked down".
B. Docker provides a secured, lightweight method to sandbox chaincode execution that is "locked down" but additional programming languages cannot be enabled
C. Docker provides a secured, lightweight method to sandbox chaincode execution that is not "locked down"
D. Docker is fully compatible with Hyperledger and Linux with an upgrade subscription.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Docker provides a secured, lightweight method to sandbox chaincode execution that is "locked down" Chaincode Services uses Docker to host (deploy) the chaincode without relying on any virtual machine or computer language. Docker provides a secured, lightweight method to sandbox chaincode execution. The environment is a "locked down" and secured container, along with a set of signed base images containing secure OS and chaincode language, runtime and SDK images for Golang Additional programming languages can be enabled
NEW QUESTION: 2
An administrator enabled the Pre-auth check for their guest self-registration. At what stage in the registration process is this check performed?
A. After the user logs in; before the NAD sends an authentication request.
B. When a user is re-authenticating to the network.
C. Before the user self-registers.
D. After the user logs in; after the NAD sends an authentication request.
E. After the user self-registers; before the user logs in.
Answer: A
NEW QUESTION: 3
Your network contains an Active Directory domain named contoso.com. The domain contains 1,000 client computers that run Windows 8.1 and 1,000 client computers that run Windows 10.
You deploy a Windows Server Update Services (WSUS) server. You create a computer group for each organizational unit (OU) that contains client computers. You configure all of the client computers to receive updates from WSUS.
You discover that all of the client computers appear in the Unassigned Computers computer group in the Update Services console.
You need to ensure that the client computers are added automatically to the computer group that corresponds to the location of the computer account in Active Directory.
Which two actions should you perform? Each correct answer presents part of the solution.
A. From Group Policy objects (GPOs), configure the Enable client-side targeting setting.
B. From the Update Services console, configure the Computers option.
C. From Active Directory Users and Computers, create a domain local distribution group for each WSUS computer group.
D. From Active Directory Users and Computers, modify the flags attribute of each OU.
Answer: A,B
Explanation:
Explanation/Reference:
Explanation:
References:
https://technet.microsoft.com/en-us/library/dd252762.aspx
https://technet.microsoft.com/en-us/library/cc720433(v=ws.10).aspx
NEW QUESTION: 4
CORRECT TEXT
Problem Scenario 77 : 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_id , order_item_order_ld ,
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 orderid in Spark and Python
3 . Calculate total revenue perday and per order
4. Calculate total and average revenue for each date. - combineByKey
-aggregateByKey
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_items -m1
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_order_items/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("p92_orders") orderltems = 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
ordersKeyValue = 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 orders Key Value']
Step 6 : Now fetch selected values Orderld, Order date and amount collected on this order.
//Retruned row will contain ((order_date,order_id),amout_collected)
revenuePerDayPerOrder = joinedData.map(lambda row: ((row[1][1].split(M,M)[1],row[0]}, float(row[1][0].split(",")[4])))
#print the result
for line in revenuePerDayPerOrder.collect():
print(line)
Step 7 : Now calculate total revenue perday and per order
A. Using reduceByKey
totalRevenuePerDayPerOrder = revenuePerDayPerOrder.reduceByKey(lambda
runningSum, value: runningSum + value)
for line in totalRevenuePerDayPerOrder.sortByKey().collect(): print(line)
#Generate data as (date, amount_collected) (Ignore ordeMd)
dateAndRevenueTuple = totalRevenuePerDayPerOrder.map(lambda line: (line[0][0], line[1])) for line in dateAndRevenueTuple.sortByKey().collect(): print(line)
Step 8 : Calculate total amount collected for each day. And also calculate number of days.
# Generate output as (Date, Total Revenue for date, total_number_of_dates)
# Line 1 : it will generate tuple (revenue, 1)
# Line 2 : Here, we will do summation for all revenues at the same time another counter to maintain number of records.
#Line 3 : Final function to merge all the combiner
totalRevenueAndTotalCount = dateAndRevenueTuple.combineByKey( \
lambda revenue: (revenue, 1), \
lambda revenueSumTuple, amount: (revenueSumTuple[0] + amount, revenueSumTuple[1]
+ 1), \
lambda tuplel, tuple2: (round(tuple1[0] + tuple2[0], 2}, tuple1[1] + tuple2[1]) \ for line in totalRevenueAndTotalCount.collect(): print(line)
Step 9 : Now calculate average for each date
averageRevenuePerDate = totalRevenueAndTotalCount.map(lambda threeElements:
(threeElements[0], threeElements[1][0]/threeElements[1][1]}}
for line in averageRevenuePerDate.collect(): print(line)
Step 10 : Using aggregateByKey
#line 1 : (Initialize both the value, revenue and count)
#line 2 : runningRevenueSumTuple (Its a tuple for total revenue and total record count for each date)
# line 3 : Summing all partitions revenue and count
totalRevenueAndTotalCount = dateAndRevenueTuple.aggregateByKey( \
(0,0), \
lambda runningRevenueSumTuple, revenue: (runningRevenueSumTuple[0] + revenue, runningRevenueSumTuple[1] + 1), \ lambda tupleOneRevenueAndCount, tupleTwoRevenueAndCount:
(tupleOneRevenueAndCount[0] + tupleTwoRevenueAndCount[0],
tupleOneRevenueAndCount[1] + tupleTwoRevenueAndCount[1]) \
)
for line in totalRevenueAndTotalCount.collect(): print(line)
Step 11 : Calculate the average revenue per date
averageRevenuePerDate = totalRevenueAndTotalCount.map(lambda threeElements:
(threeElements[0], threeElements[1][0]/threeElements[1][1]))
for line in averageRevenuePerDate.collect(): print(line)