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NEW QUESTION: 1
Your network contains an Active Directory domain named contoso.com.
You plan to deploy an application named App1.exe.
You need to verify whether Control Flow Guard is enabled for App1.exe.
Which command should you run? To answer, select the appropriate options in the answer area.

Answer:
Explanation:

Explanation

ttps://msdn.microsoft.com/en-us/library/windows/desktop/mt637065(v=vs.85).aspxControl Flow Guard (CFG) is a highly-optimized platform security feature that was created to combat memorycorruption vulnerabilities.
By placing tight restrictions on where an application can execute code from, it makes it much harder for exploitsto execute arbitrary code through vulnerabilitiessuch as buffer overflows.To verify if Control Flow Guard is enable for a certain application executable:-Run the dumpbin.exe tool (included in the Visual Studio
2015 installation) from the Visual Studio commandprompt with the /headers and /loadconfig options:
dumpbin.exe /headers /loadconfig test.exe.The output for a binary under CFG should show that the header values include "Guard", and that the loadconfig values include "CF Instrumented" and "FID tablepresent".1

NEW QUESTION: 2
What is the maximum number of E-MSM controllers that can be configured into a single team?
A. 0
B. 1
C. 2
D. 3
E. 4
Answer: B

NEW QUESTION: 3
CORRECT TEXT
Problem Scenario 46 : You have been given belwo list in scala (name,sex,cost) for each work done.
List( ("Deeapak" , "male", 4000), ("Deepak" , "male", 2000), ("Deepika" , "female",
2000),("Deepak" , "female", 2000), ("Deepak" , "male", 1000) , ("Neeta" , "female", 2000))
Now write a Spark program to load this list as an RDD and do the sum of cost for combination of name and sex (as key)
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create an RDD out of this list
val rdd = sc.parallelize(List( ("Deeapak" , "male", 4000}, ("Deepak" , "male", 2000),
("Deepika" , "female", 2000),("Deepak" , "female", 2000), ("Deepak" , "male", 1000} ,
("Neeta" , "female", 2000}}}
Step 2 : Convert this RDD in pair RDD
val byKey = rdd.map({case (name,sex,cost) => (name,sex)->cost})
Step 3 : Now group by Key
val byKeyGrouped = byKey.groupByKey
Step 4 : Nowsum the cost for each group
val result = byKeyGrouped.map{case ((id1,id2),values) => (id1,id2,values.sum)}
Step 5 : Save the results result.repartition(1).saveAsTextFile("spark12/result.txt")