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NEW QUESTION: 1
What does it mean when a purchase contract has a status of WSTART?
A. WSTART is not a valid status for a purchase contract.
B. The contract is waiting for approval to start.
C. The start date of this contract is in the future.
D. The contract has been revised to start.
Answer: C

NEW QUESTION: 2
What types of algorithms are difficult to express in MapReduce v1 (MRv1)?
A. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
B. Algorithms that require global, sharing states.
C. Large-scale graph algorithms that require one-step link traversal.
D. Relational operations on large amounts of structured and semi-structured data.
E. Algorithms that require applying the same mathematical function to large numbers of individual binary records.
Answer: B
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
Microsoft 365サブスクリプションがあります。
次の図に示すように、危険なIPアドレステンプレートからのログオンに基づいて、Risk1という名前のMicrosoft Cloud App Securityポリシーを作成します。

User1とUser2という名前の2人のユーザーがいます。各ユーザーは、危険なIPアドレスから24時間以内にMicrosoft SharePoint Onlineに10回サインインします。
ドロップダウンメニューを使用して、図に示されている情報に基づいて各ステートメントを完了する回答の選択肢を選択します。
注：それぞれの正しい選択は1ポイントの価値があります。

Answer:
Explanation: