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NEW QUESTION: 1
You need to recommend a solution for managing updates. The solution must meet the technical requirements.
What should you include in the recommendation?
A. A System Center 2012 Configuration Manager management point in the main office and a WSUS downstream server in each office
B. A System Center 2012 Configuration Manager management point in the main office and a System Center 2012 Configuration Manager distribution point in each office
C. A WSUS upstream server in the main office and a WSUS downstream server in each office
D. A System Center 2012 Configuration Manager software update point in the main office and a System Center 2012 Configuration Manager distribution point in each office
Answer: D
Explanation:
Topic 3, Northwind Traders
Overview
Northwind Traders is an IT services and hosting provider.
Northwind Traders has two main data centers in North America. The data centers are located in the same city.
The data centers connect to each other by using high-bandwidth, low-latency WAN links. Each data center connects directly to the Internet.
Northwind Traders also has a remote office in Asia that connects to both of the North American data centers by using a WAN link. The Asian office has 30 multipurpose servers.
Each North American data center contains two separate network segments. One network segment is used to host the internal servers of Northwind Traders. The other network segment is used for the hosted customer environments.
Existing Environment
Active Directory
The network contains an Active Directory forest named northwindtraders.com. The forest contains a single domain. All servers run Windows Server 2012 R2.
Server Environment
The network has the following technologies deployed:
* Service Provider Foundation
* Windows Azure Pack for Windows Server
* System Center 2012 R2 Virtual Machine Manager (VMM)
* An Active Directory Rights Management Services (AD RMS) cluster
* An Active Directory Certificate Services (AD CS) enterprise certification authority (CA) All newly deployed servers will include the following components:
* Dual 10-GbE Remote Direct Memory Access (RDMA)-capable network adapters
* Dual 1-GbE network adapters
* 128 GB of RAM
Requirements
Business Goals
Northwind Traders will provide hosting services to two customers named Customer1 and Customer2. The network of each customer is configured as shown in the following table.

Planned Changes
Northwind Traders plans to implement the following changes:
* Deploy System Center 2012 R2 Operations Manager.
* Deploy Windows Server 2012 R2 iSCSI and SMB-based storage.
* Implement Hyper-V Recovery Manager to protect virtual machines.
* Deploy a certificate revocation list (CRL) distribution point (CDP) on the internal network.
* For Customer 1, install server authentication certificates issued by the CA of Northwind Traders on the virtual machine in the hosting networks.
General Requirements
Northwind Traders identifies the following requirements:
* Storage traffic must use dedicated adapters.
* All storage and network traffic must be load balanced.
* The amount of network traffic between the internal network and the hosting network must be minimized.
* The publication of CRLs to CDPs must be automatic.
* Each customer must use dedicated Hyper-V hosts.
* Administrative effort must be minimized, whenever possible.
* All servers and networks must be monitored by using Operations Manager.
* Anonymous access to internal file shares from the hosting network must be prohibited.
* All Hyper-V hosts must use Cluster Shared Volume (CSV) shared storage to host virtual machines.
* All Hyper-V storage and network traffic must remain available if single network adapter fails.
* The Hyper-V hosts connected to the SMB-based storage must be able to make use of the RDMA technology.
* The number of servers and ports in the hosting environment to which the customer has access must be minimized.
Customer1 Requirements
Northwind Traders identifies the following requirements for Customer1:
* Customer1 must use SMB-based storage exclusively.
* Customer1 must use App Controller to manage hosted virtual machines.
* The virtual machines of Customer1 must be recoverable if a single data center fails.
* Customer1 must be able to delegate self-service roles in its hosted environment to its users.
* Customer1 must be able to check for the revocation of certificates issued by the CA of Northwind Traders.
* The users of Customer1 must be able to obtain use licenses for documents protected by the AD RMS of Northwind Traders.
* Certificates issued to the virtual machines of Customer1 that reside on the hosted networks must be renewed automatically.
Customer2 Requirements
Northwind Traders identifies the following requirements for Customer2:
* Customer2 must use iSCSI-based storage exclusively.
* All of the virtual machines of Customer2 must be migrated by using a SAN transfer.
* None of the metadata from the virtual machines of Customer2 must be stored in Windows Azure.
* The network configuration of the Hyper-V hosts for Customer2 must be controlled by using logical switches.
* The only VMM network port profiles and classifications allowed by Customer2 must be low-bandwidth, medium-bandwidth, or high-bandwidth.
* The users at Northwind Traders must be able to obtain use licenses for documents protected by the AD RMS cluster of Customer2. Customer2 plans to decommission its AD RMS cluster during the next year.

NEW QUESTION: 2
CORRECT TEXT
Problem Scenario 68 : You have given a file as below.
spark75/f ile1.txt
File contain some text. As given Below
spark75/file1.txt
Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework
The core of Apache Hadoop consists of a storage part known as Hadoop Distributed File
System (HDFS) and a processing part called MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a cluster. To process data, Hadoop transfers packaged code for nodes to process in parallel based on the data that needs to be processed.
his approach takes advantage of data locality nodes manipulating the data they have access to to allow the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking
For a slightly more complicated task, lets look into splitting up sentences from our documents into word bigrams. A bigram is pair of successive tokens in some sequence.
We will look at building bigrams from the sequences of words in each sentence, and then try to find the most frequently occuring ones.
The first problem is that values in each partition of our initial RDD describe lines from the file rather than sentences. Sentences may be split over multiple lines. The glom() RDD method is used to create a single entry for each document containing the list of all lines, we can then join the lines up, then resplit them into sentences using "." as the separator, using flatMap so that every object in our RDD is now a sentence.
A bigram is pair of successive tokens in some sequence. Please build bigrams from the sequences of words in each sentence, and then try to find the most frequently occuring ones.
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create all three tiles in hdfs (We will do using Hue}. However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : The first problem is that values in each partition of our initial RDD describe lines from the file rather than sentences. Sentences may be split over multiple lines.
The glom() RDD method is used to create a single entry for each document containing the list of all lines, we can then join the lines up, then resplit them into sentences using "." as the separator, using flatMap so that every object in our RDD is now a sentence.
sentences = sc.textFile("spark75/file1.txt") \ .glom() \
map(lambda x: " ".join(x)) \ .flatMap(lambda x: x.spllt("."))
Step 3 : Now we have isolated each sentence we can split it into a list of words and extract the word bigrams from it. Our new RDD contains tuples containing the word bigram (itself a tuple containing the first and second word) as the first value and the number 1 as the second value. bigrams = sentences.map(lambda x:x.split())
\ .flatMap(lambda x: [((x[i],x[i+1]),1)for i in range(0,len(x)-1)])
Step 4 : Finally we can apply the same reduceByKey and sort steps that we used in the wordcount example, to count up the bigrams and sort them in order of descending frequency. In reduceByKey the key is not an individual word but a bigram.
freq_bigrams = bigrams.reduceByKey(lambda x,y:x+y)\
map(lambda x:(x[1],x[0])) \
sortByKey(False)
freq_bigrams.take(10)

NEW QUESTION: 3



A. Option D
B. Option A
C. Option C
D. Option B
E. Option E
Answer: D