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NEW QUESTION: 1
フォレンジックアナリストは、サーバーで進行中のネットワーク攻撃に対応するよう求められます。フォレンジックアナリストが保存する正しい順序で、以下のリストのアイテムを配置します。

Answer:
Explanation:

Explanation:
When dealing with multiple issues, address them in order of volatility (OOV); always deal with the most volatile first. Volatility can be thought of as the amount of time that you have to collect certain data before a window of opportunity is gone. Naturally, in an investigation you want to collect everything, but some data will exist longer than others, and you cannot possibly collect all of it once. As an example, the OOV in an investigation may be RAM, hard drive data, CDs/DVDs, and printouts.
Order of volatility: Capture system images as a snapshot of what exists, look at network traffic and logs, capture any relevant video/screenshots/hashes, record time offset on the systems, talk to witnesses, and track total man-hours and expenses associated with the investigation.

NEW QUESTION: 2
In which form the data is stored using shared preferences?
A. A Text file inside internal memory of application.
B. Binary form inside the internal memory of application.
C. Encrypted form inside the application's internal memory.
D. XML file inside the internal memory of application.
Answer: D
Explanation:
Explanation/Reference:

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
Process-level redundancy is implemented by a system manager process that creates the standby process. What two functions are provided by the system-level process called Qnet Symlink Manager (QSM)? (Choose two.)
A. distribution of symbolic link information
B. detection of a failed connection
C. provides common information for connecting processes and services
D. provides an abstract name for a process or service
E. backing up the information for the broken connections
Answer: A,D
Explanation:
1.4. SP high end product
1.4.01. IOS-XR structure Process-Level Redundancy Process-level redundancy is implemented by a system manager process creating the standby process. Because the active process created the standby process, the active process has all the information that it needs to communicate with the standby process. The active process uses a checkpoint database to share running state with the standby process. Symbolic links and abstract names are used to identify the processes. Clients do not see the standby process until the active goes away. If a process fails and it has created a standby process, a system-level process called QNet Symlink Manager (QSM) and a library called Event Connection Manager (ECM) are used to re-establish links from the clients to the processes.
QSM provides: Distribution of symbolic link information Abstract name for a process or service
ECM provides:
Common information for connecting processes and services
Detection of broken connections
Only processes considered essential by development engineers are designated to support
process-level redundancy. This is not a user-configurable option.
Clients have to reconnect to the "new" active process (the "original" standby process) when
they detect that the active process has failed. Clients can connect to it using the symbolic
links and abstract names. The new active process creates a new standby process.
The general steps in process redundancy are:
The active process dies.
The standby process becomes the active process.
A new standby process starts.
The new active process begins sending updates to the new standby process.
Clients begin using the new active process through the symbolic links and abstract names.