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NEW QUESTION: 1
Which bit should be set in the link-state PDU of an IS-IS L1/L2 router to indicate that it is a potential exit point of the area?
A. the ABR bit
B. the ATT bit
C. the down bit
D. the P bit
Answer: B
Explanation:
Default routing is achieved in two distinct ways with Integrated IS-IS:
Attached bit-Set by a Level 1/Level 2 router in its own Level 1 LSP and used to indicate to all Level 1 routers (within the area) that this router is a potential exit point of the area. Level 1-only routers will default to the nearest attached Level 2 router.
Default information originate-Can be configured in Level 1 as well as Level 2. The default route (0.0.0.0/0) is inserted in the router LSP (Level 1 or Level 2, according to the configuration command) and the LSP is flooded according to the router type (Level 1 or Level 2). A Level 2 router doesn't need to have a default route to originate a default route. ReferencE. http://www.cisco.com/en/US/products/ps6599/products_white_paper09186a00800a3e6f.shtml

NEW QUESTION: 2
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. Default Partitioner
C. IdentityMapper
D. Speculative Execution
E. IdentityReducer
Answer: D
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.
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: 3
What data is shared between all SAP Ariba modules?
A. Teams
B. Commodity codes
C. Units of measure
D. Documents
E. Groups
Answer: B,C,E