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NEW QUESTION: 1
The Hadoop framework provides a mechanism for coping with machine issues such as faulty configuration or impending hardware failure. MapReduce detects that one or a number of machines are performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and the task finish first are used. This is called:
A. Combine
B. IdentityMapper
C. Speculative Execution
D. Default Partitioner
E. IdentityReducer
Answer: C
Explanation:
Explanation/Reference:
Speculative execution: One problem with the Hadoop system is that by dividing the tasks across many nodes, it is possible for a few slow nodes to rate-limit the rest of the program. For example if one node has a slow disk controller, then it may be reading its input at only 10% the speed of all the other nodes. So when 99 map tasks are already complete, the system is still waiting for the final map task to check in, which takes much longer than all the other nodes.
By forcing tasks to run in isolation from one another, individual tasks do not know where their inputs come from. Tasks trust the Hadoop platform to just deliver the appropriate input. Therefore, the same input can be processed multiple times in parallel, to exploit differences in machine capabilities. As most of the tasks in a job are coming to a close, the Hadoop platform will schedule redundant copies of the remaining tasks across several nodes which do not have other work to perform. This process is known as speculative execution. When tasks complete, they announce this fact to the JobTracker. Whichever copy of a task finishes first becomes the definitive copy. If other copies were executing speculatively, Hadoop tells the TaskTrackers to abandon the tasks and discard their outputs. The Reducers then receive their inputs from whichever Mapper completed successfully, first.
Reference: Apache Hadoop, Module 4: MapReduce
Note:
* Hadoop uses "speculative execution." The same task may be started on multiple boxes. The first one to finish wins, and the other copies are killed.
Failed tasks are tasks that error out.
* There are a few reasons Hadoop can kill tasks by his own decisions:
a) Task does not report progress during timeout (default is 10 minutes) b) FairScheduler or CapacityScheduler needs the slot for some other pool (FairScheduler) or queue (CapacityScheduler).
c) Speculative execution causes results of task not to be needed since it has completed on other place.
Reference: Difference failed tasks vs killed tasks

NEW QUESTION: 2
Which configuration allows you to block calls based on ANI 4085267209?
A.
B.
C.
D.
Answer: D

NEW QUESTION: 3
What setting is defined with a movement type in SAP Materials Management?
Please choose the correct answer.
Response:
A. Valuation area
B. Account modification
C. Count group
D. Valuation class
Answer: B

NEW QUESTION: 4
Was tun regionale Einrichtungen im Stil der Financial Action Task Force (FATF) für ihre Mitglieder, um zur Bekämpfung von Geldwäsche und Terrorismusfinanzierung beizutragen?
A. Sie bieten den Mitgliedern technische Unterstützung bei der Umsetzung der FATF-Empfehlungen
B. Sie unterstützen die Mitgliedsländer bei der Bestrafung von Stellen, die gegen FATF-Standards und -Empfehlungen verstoßen
C. Sie arbeiten mit Mitgliedern in Problembereichen außerhalb der Bekämpfung von Geldwäsche und Terrorismusfinanzierung zusammen
D. Sie beaufsichtigen Finanzinstitutionen der Mitgliedstaaten im Zusammenhang mit der Bekämpfung von Geldwäsche und Terrorismusfinanzierung
Answer: D