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NEW QUESTION: 1
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. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
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: A
Explanation:
Explanation/Reference:
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
仮想スイッチングシステム「VSS」には何が必要ですか？
A. 仮想スイッチングリンク「またはVSL」
B. ポートチャネル
C. 仮想トランク
D. トランク
Answer: A

NEW QUESTION: 3
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
オンプレミスリソースとAzureがホストするリソースをサポートするストレージソリューションを設計しています。
Azureへのレプリケーションが組み込まれたオンプレミスストレージを提供する必要があります。
ソリューション：デザインにAzure Blobストレージを含めます。
ソリューションは目標を達成していますか？
A. いいえ
B. はい
Answer: A

NEW QUESTION: 4
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question on this series.
You have a database that tracks orders and deliveries for customers in North America. System versioning is enabled for all tables. The database contains the Sales.Customers, Application.Cities, and Sales.CustomerCategories tables.
Details for the Sales.Customers table are shown in the following table:

Details for the Application.Cities table are shown in the following table:

Details for the Sales.CustomerCategories table are shown in the following table:

The marketing department is performing an analysis of how discount affect credit limits. They need to know the average credit limit per standard discount percentage for customers whose standard discount percentage is between zero and four.
You need to create a query that returns the data for the analysis.
How should you complete the Transact-SQL statement? To answer, drag the appropriate Transact-SQL segments to the correct locations. Each Transact-SQL segments may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Answer:
Explanation:

Explanation

Box 1: 0, 1, 2, 3, 4
Pivot example:
-- Pivot table with one row and five columns
SELECT 'AverageCost' AS Cost_Sorted_By_Production_Days,
[0], [1], [2], [3], [4]
FROM
(SELECT DaysToManufacture, StandardCost
FROM Production.Product) AS SourceTable
PIVOT
(
AVG(StandardCost)
FOR DaysToManufacture IN ([0], [1], [2], [3], [4])
) AS PivotTable;
Box 2: [CreditLimit]
Box 3: PIVOT
You can use the PIVOT and UNPIVOT relational operators to change a table-valued expression into another table. PIVOT rotates a table-valued expression by turning the unique values from one column in the expression into multiple columns in the output, and performs aggregations where they are required on any remaining column values that are wanted in the final output.
Box 4: 0, 1, 2, 3, 4
The IN clause determines whether a specified value matches any value in a subquery or a list.
Syntax: test_expression [ NOT ] IN ( subquery | expression [ ,...n ] )
Where expression[ ,... n ]
is a list of expressions to test for a match. All expressions must be of the same type as test_expression.
References: https://technet.microsoft.com/en-us/library/ms177410(v=sql.105).aspx