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NEW QUESTION: 1
With which two options can Mapping Sets validate input values?
A. Accounting Segment
B. FlexFields
C. Value Sets
D. Lookup Types
Answer: C,D
Explanation:
Mapping sets enable you to assign a specific output value to an Accounting Flexfield
or Accounting Flexfield segment. You use mapping sets when you set up account derivation rules.
Account derivation rules determine the Accounting Flexfield values for subledger journal entries.
Oracle Projects does not provide any predefined mapping sets. You can optionally define your
own mapping sets. When you enter input values for mapping sets, you can select from a list of
values based on either an existing lookup set or value set. You also specify the Accounting
Flexfield segment and select segment values from a list of values.
For example, you can select a lookup type of service type for the input and the Accounting
Flexfield segment program as the output. You then select the service type and program segment
values from lists of values as you define each pair
Reference: Oracle Projects Implementation Guide, Mapping Sets

NEW QUESTION: 2
When logging in to the SMC2.0 operating system for the first time, the system administrator defaults to the username admin, The password is Chang_Me. ()
A. TRUE
B. FALSE
Answer: B

NEW QUESTION: 3
Which three business users can submit a new supplier request? (Choose three.)
A. Self-Service Procurement user
B. Supplier Administrator
C. Category Manager
D. Warehouse Manager
E. Catalog Administrator
Answer: A,B,E

NEW QUESTION: 4
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. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
C. Algorithms that require global, sharing states.
D. Relational operations on large amounts of structured and semi-structured data.
E. Large-scale graph algorithms that require one-step link traversal.
Answer: C
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