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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet stated goals. Some questions sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You use a Microsoft Azure SQL database as a data warehouse. The database is in the Standard service tier and has 400 elastic database throughput units (eDTUs).
You load data to the database by using Azure Data Factory.
You need to reduce the amount of time it takes to load the data.
Solution: You move the database to a Basic database pool that has 1,600 eDTUs.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Explanation/Reference:
Explanation:
We need the use of a Standard database pool.

NEW QUESTION: 2
What types of algorithms are difficult to express MapReduce?
A. Text analysis algorithms on large collections of unstructured text (e.g., Web crawls).
B. Relational operations on large amounts of structured and semi structured data.
C. Algorithms that requite global, shared state.
D. Algorithms that require applying the same mathematical function to large numbers of individual binary records.
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: 3
An administrator needs to optimize traffic to prefer business-critical applications over non-critical applications.
QoS natively integrates with which feature to provide service quality?
A. Content-ID
B. App-ID
C. certification revocation
D. port inspection
Answer: B

NEW QUESTION: 4
What is a function of business relationship management?
A. Determine if the capacity of IT services can deliver the agreed upon service levels
B. Determine what the sufficient capacity is to meet business demand
C. Identity requirements and ensure that the appropriate services are provided
D. Identify accurate information for all operational services
Answer: A