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NEW QUESTION: 1
What types of algorithms are difficult to express in MapReduce v1 (MRv1)?
A. Algorithms that require applying the same mathematical function to large numbers of
individual binary records.
B. Relational operations on large amounts of structured and semi-structured data.
C. Algorithms that require global, sharing states.
D. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
E. Large-scale graph algorithms that require one-step link traversal.
Answer: C
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: 2
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以下の最大速度を対応するワイヤレス規格にドラッグアンドドロップします。
トークンは複数回使用できます。

Answer:
Explanation:

NEW QUESTION: 3

public static void Log(Exception ex) { }


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

NEW QUESTION: 4
Your company plans to migrate from On-Premises Exchange to Exchange Online in Office
365.
You plan to integrate your existing Active Directory Domain Services (AD DS) infrastructure with Azure AD.
You need to ensure that users can log in by using their existing AD DS accounts and passwords. You need to achieve this goal by using minimal additional systems.
Which two actions should you perform? Each answer presents part of the solution.
A. Set up an Active Directory Federation Services Proxy Server.
B. Set up a DirSync Server.
C. Configure Password Sync.
D. Set up an Active Directory Federation Services Server.
Answer: B,C
Explanation:
Directory Sync with Password Sync Scenario' Applies To: Azure, Office 365, Windows Intune
Password sync is an extension to the Directory Sync Scenario. With directory sync, you can manage the entire lifecycle of your cloud user and group accounts using your on-premise Active Directory management tools.
When password sync is enabled on your directory sync computer, your users will be able to sign into Microsoft cloud services, such as Office 365, Dynamics CRM, and Windows InTune, using the same password as they use when logging into your on-premises network. When your users change their passwords in your corporate network, those changes are synchronized to the cloud.
Reference: Directory Sync with Password Sync Scenario' URL: http://technet.microsoft.com/en-us/library/dn441214.aspx