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  • Exam Name: AWS Certified Security - Specialty (SCS-C01 Korean Version)

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NEW QUESTION: 1
トンネルの宛先が到達不能になった場合、GREトンネルインターフェイスのラインプロトコルステータスを正常にダウンさせる構成はどれですか?
A. トンネルをループバックインターフェイスに置き換えます
B. トンネルインターフェイスキープアライブを設定します。
C. トンネルでOSPFなどのIGPを構成します
D. トンネルの静的ルートを構成します
Answer: A

NEW QUESTION: 2
HOTSPOT
You have a Hyper-V host named Server1 that runs Windows Server 2016. Server 1 has a virtual switch Switch1.
Server1 hosts the virtual machines configured as shown in the following table.

Windows firewall on VM1 and VM2 is configured to allow ICMP traffic. VM1 and VM2 connect to Switch1.
You fail to ping VM1 from VM2.
You need to view the VirtualSubnetid to which VM1 connects.
Which cmdlet should you run on Server1.

Answer:
Explanation:
Get-VMNetworkAdapter-VMName VM1 | forest-list

NEW QUESTION: 3
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)

NEW QUESTION: 4
Refer to exhibits.
Exhibit A.

Exhibit B.

Exhibit A shows the SD-WAN rules and exhibit B shows the traffic logs. The SD-WAN traffic logs reflect how FortiGate processed traffic.
Which two statements about how the configured SD-WAN rules are processing traffic are true? (Choose two.)
A. The All_Access_Rules rule load balances Vimeo application traffic among SD-WAN member interfaces
B. The implicit rule overrides all other rules because parameters widely cover sources and destinations.
C. The initial session of an application goes through a learning phase in order to apply the correct rule
D. SD-WAN rules are evaluated in the same way as firewall policies: from top to bottom
Answer: B,C

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