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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. Default Partitioner
D. IdentityReducer
E. Speculative Execution
Answer: E
Explanation:
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.
*There are a few reasons Hadoop can kill tasks by his own decisions:
Failed tasks are tasks that error out.
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
An architect needs to scope the number of required m710p cartridges for video transcoding. The architect plans to aim for an additional 20 to 25 percent growth capacity.
In addition to this growth capacity, which factors will determine the number of required cartridges? (Choose two.)
A. the amount of storage capacity required
B. the number of formats to which to convert
C. the amount of memory required
D. the number and type of processors
E. the number of video streams required
Answer: A,E

NEW QUESTION: 3
サービス管理のどの側面が、サービスを提供するために必要なワークフローと制御を考慮していますか？
A. パートナーとサプライヤー
B. 組織と人々
C. バリューストリームとプロセス
D. 情報とテクノロジー
Answer: C

NEW QUESTION: 4
A network technician has been tasked with connecting three 802.11a clients to an 802.11g access point
that is configured for WEP and is within a (9m) line of sight from the client. The clients are unable to
associate with the access point.
Which of the following is MOST likely the cause of this issue?
A. Wrong Encryption
B. Wrong frequency
C. Signal loss
D. Interference
Answer: B
Explanation:
Explanation/Reference:
Explanation:
802.11a operates in the 5GHz band while 802.11g operates in the 2.4GHz band.