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NEW QUESTION: 1
Welcher der folgenden Befehle ändert alle Vorkommen des Wortes "bob" in den Dateidaten in "Bob" und gibt das Ergebnis in der Standardausgabe aus?
A. sed '/ bob / Bob /' data
B. Daten von sed / bob / Bob / g
C. sed's / bob / Bob / 'Daten
D. sed '/ bob / Bob' Daten
E. sed's / bob, Bob / 'Daten
Answer: B
NEW QUESTION: 2
A. Option C
B. Option D
C. Option B
D. Option A
Answer: B
Explanation:
Explanation
You can set a limit of how many virtual terminals that can simultaneously be connected to a switch. Here the limit is set to one, and we should increase it.
For a Cisco network device:
You can use virtual terminal lines to connect to your Cisco NX-OS device, for example a switch. Secure Shell (SSH) and Telnet create virtual terminal sessions. You can configure an inactive session timeout and a maximum sessions limit for virtual terminals.
session-limit sessions
Example:
switch(config-line)# session-limit 10
Configures the maximum number of virtual sessions for the Cisco NX-OS device. The range is from 1 to 64.
NEW QUESTION: 3
Your OSM solution fulfills orders originating from two different countries, C1 and C2. Two roles, R1 and R2, are created in your cartridges. Which design feature can you use to enable users with role R1 to view and manage orders only from country C1 and users with role R2 to view and mange orders only from country C2?
A. Query
B. Task Data
C. Roles, which alone are sufficient to meet the desired functionality
D. Filters
E. Order Data
Answer: A
NEW QUESTION: 4
What types of algorithms are difficult to express MapReduce?
A. Large-scale graph algorithms that require one-step link traversal.
B. Algorithms that require applying the same mathematical function to large numbers of individual binary records.
C. Algorithms that requite global, shared state.
D. Text analysis algorithms on large collections of unstructured text (e.g., Web crawls).
E. Relational operations on large amounts of structured and semi structured data.
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