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NEW QUESTION: 1
Northern Trail Outfitters has its sales support team enter new prospecting leads for sales representatives.
Which three actions should be implemented to enforce data quality and accuracy once the new lead has been qualified and the opportunity has been created to track the deal? Choose 3 answers
A. Create an Apex trigger to perform data quality checks.
B. Enable validation rules on the lead
C. Enable validation rules on the opportunity
D. Enable the lead conversion permission
E. Map custom fields to corresponding opportunity fields
Answer: B,D,E

NEW QUESTION: 2
Ein Unternehmen verfügt über 100 Windows 10 Enterprise-Clientcomputer. Sie müssen den Clientcomputern statische IPv6-Adressen zuweisen. Welches Windows PowerShell-Cmdlet sollten Sie ausführen?
A. Set-NetlPAddress
B. Set-NetIPv6Protocol
C. Set-NctTCPSetting
D. Set-NetlPInterface
Answer: A

NEW QUESTION: 3
When configure HSPR on IPv6 enabled interface, which two configuration is correct.
A. switchA(config-if)ff key 6
B. switchA(config-if) hsrp <group-number>
C. switchA{config-if) hsrp version 2
D. switchA{config-if) standbyt 6 preempt
E. switchA{config-if)B priority <level>
Answer: B

NEW QUESTION: 4
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. Default Partitioner
B. Combine
C. IdentityReducer
D. Speculative Execution
E. IdentityMapper
Answer: D
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