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NEW QUESTION: 1
You are configuring a virtual environment. The environment includes servers that run either Windows Server 2003 or Windows Server 2008 R2. You manage the environment by using Microsoft System Center Virtual Machine Manager (VMM) 2008 R2.
The servers that run Windows Server 2003 do not meet the system requirements to run Windows Server 2008 R2 or Microsoft Hyper-V Server 2008 R2.
You want to host non-production virtual machines (VMS) on the Windows Server 2003 servers.
You need to be able to manage the Windows Server 2003 servers by using VMM.
What should you do?
A. Install Virtual Machine Remote Control Client Plus (VMRCplus) on the Windows Server 2003 host servers.
B. Stage the Microsoft Virtual Server 2005 R2 software on the VMM server.
C. Stage the Microsoft Virtual Server 2005 software on the VMM server.
D. Add the Windows Server 2003 host servers to VMM by using the Add Hosts wizard.
Answer: D

NEW QUESTION: 2
What types of algorithms are difficult to express in MapReduce v1 (MRv1)?
A. Algorithms that require global, sharing states.
B. Large-scale graph algorithms that require one-step link traversal.
C. Algorithms that require applying the same mathematical function to large numbers of individual binary records.
D. Relational operations on large amounts of structured and semi-structured data.
E. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
Answer: A
Explanation:
See 3) below.
Limitations of Mapreduce - where not to use Mapreduce
While very powerful and applicable to a wide variety of problems, MapReduce is not the
answer to every problem. Here are some problems I found where MapReudce is not suited
and some papers that address the limitations of MapReuce.
1.Computation depends on previously computed values If the computation of a value depends on previously computed values, then MapReduce cannot be used. One good example is the Fibonacci series where each value is summation of the previous two values. i.e., f(k+2) = f(k+1) + f(k). Also, if the data set is small enough to be computed on a single machine, then it is better to do it as a single reduce(map(data)) operation rather than going through the entire map reduce process.
2.Full-text indexing or ad hoc searching The index generated in the Map step is one dimensional, and the Reduce step must not generate a large amount of data or there will be a serious performance degradation. For example, CouchDB's MapReduce may not be a good fit for full-text indexing or ad hoc searching. This is a problem better suited for a tool such as Lucene.
3.Algorithms depend on shared global state Solutions to many interesting problems in text processing do not require global synchronization. As a result, they can be expressed naturally in MapReduce, since map and reduce tasks run independently and in isolation. However, there are many examples of
algorithms that depend crucially on the existence of shared global state during processing, making them difficult to implement in MapReduce (since the single opportunity for global synchronization in MapReduce is the barrier between the map and reduce phases of processing)
Reference: Limitations of Mapreduce - where not to use Mapreduce

NEW QUESTION: 3
Which RuleSet versions are accessible to an end user for the RuleSet MyCo if MyCo:07-27 is included in the runtime RuleSet list? (Choose One)
A. 07-27-01 to 07-27-99
B. 07-01-27
C. 07-27-01
D. 07-01-01 to 07-27-99
Answer: D