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NEW QUESTION: 1
Which of the following statements regarding route-policies are true?
A. A route-policy can filter only default routes
B. If the entry permit :: 0 less-equal 128 is defined, all IPv6 routes are permitted
C. An advanced ACL can be used in a route-policy to filter routes
D. If more than one node is defined in a route-policy, the matching mode of at least one node must be permit
Answer: D
NEW QUESTION: 2
Identify the hierarchy that the Oracle Enterprise Manager Compliance Framework is composed of.
A. Compliance Framework, Compliance Blueprints, Compliance Rule Sets, and Compliance Monitor
B. Compliance Framework, Compliance Rule Builder, Compliance Rule Deployer, , and Compliance
Monitor
C. Compliance Framework, Compliance Plans, Compliance Standard Rules, and Compliance Monitoring
D. Compliance Framework, Compliance Standards, Compliance Standard Rules, and real-time Monitoring
Facts
Answer: D
Explanation:
Explanation/Reference:
* A compliance framework maps to a set of compliance standards that perform a collection of checks
following broadly accepted best practices to ensure that IT infrastructure, applications, business services
and processes are organized, configured, managed, and monitored correctly.
* A compliance standard refers to one or more compliance standard rules.
Note: A compliance framework can be used to represent a framework such as Control Objectives for
Information and related Technology (COBIT) or Payment Card Industry (PCI). A compliance framework is
an industry-specified best practices guideline that deals with the underlying IT infrastructure, applications,
business services and processes, and how they are organized, managed, and monitored. Compliance
frameworks are hierarchical to allow for direct representation of these industry frameworks.
Reference: Oracle Enterprise Manager Cloud Control Extensibility Programmer's Guide, Understanding
Compliance Standards
NEW QUESTION: 3
CORRECT TEXT
Problem Scenario 33 : You have given a files as below.
spark5/EmployeeName.csv (id,name)
spark5/EmployeeSalary.csv (id,salary)
Data is given below:
EmployeeName.csv
E01,Lokesh
E02,Bhupesh
E03,Amit
E04,Ratan
E05,Dinesh
E06,Pavan
E07,Tejas
E08,Sheela
E09,Kumar
E10,Venkat
EmployeeSalary.csv
E01,50000
E02,50000
E03,45000
E04,45000
E05,50000
E06,45000
E07,50000
E08,10000
E09,10000
E10,10000
Now write a Spark code in scala which will load these two tiles from hdfs and join the same, and produce the (name.salary) values.
And save the data in multiple tile group by salary (Means each file will have name of employees with same salary). Make sure file name include salary as well.
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create all three files in hdfs (We will do using Hue). However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : Load EmployeeName.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark5/EmployeeName.csv")
val namePairRDD = name.map(x=> (x.split(",")(0),x.split('V')(1)))
Step 3 : Load EmployeeSalary.csv file from hdfs and create PairRDDs
val salary = sc.textFile("spark5/EmployeeSalary.csv")
val salaryPairRDD = salary.map(x=> (x.split(",")(0),x.split(",")(1)))
Step 4 : Join all pairRDDS
val joined = namePairRDD.join(salaryPairRDD}
Step 5 : Remove key from RDD and Salary as a Key. val keyRemoved = joined.values
Step 6 : Now swap filtered RDD.
val swapped = keyRemoved.map(item => item.swap)
Step 7 : Now groupBy keys (It will generate key and value array) val grpByKey = swapped.groupByKey().collect()
Step 8 : Now create RDD for values collection
val rddByKey = grpByKey.map{case (k,v) => k->sc.makeRDD(v.toSeq)}
Step 9 : Save the output as a Text file.
rddByKey.foreach{ case (k,rdd) => rdd.saveAsTextFile("spark5/Employee"+k)}