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NEW QUESTION: 1
When using the get feedback functionality during the performance review process, who can a user send the form to for feedback?
Please choose the correct answer.
Response:
A. Any active user
B. Users within the user's hierarchy
C. EM+ only
D. EM and EMD
Answer: A

NEW QUESTION: 2
Lab Simulation
Alter recent changes to the pictured network, several users are unable to access the servers.
Only PC1, PC2, PC3, and PC4 are clickable and will give you access to the command prompt and the adapter configuration tabs.
Instructions: Verify the settings by using the command prompt, after making any system changes.
Next, restore connectivity by making the appropriate changes to the infrastructure.
When you have completed these steps, select the Done button to submit.




Answer:
Explanation:
On PC3, change the subnet mask to 255.255.255.0.
When it is set to 255.255.255.128, then the PC with a .129 address will not be on the same subnet as the default gateway which is .1 On PC4, change the default gateway to 10.0.2.1.
It has been incorrectly set as 10.1.2.1.

NEW QUESTION: 3
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. Speculative Execution
B. Combine
C. IdentityReducer
D. IdentityMapper
E. Default Partitioner
Answer: A
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: 4
Amazon Relational Database Service (Amazon RDS) offers which of the following benefits over traditional database management?
A. AWS automatically scales up instance types on demand.
B. AWS manages the data stored in Amazon RDS tables.
C. AWS manages the maintenance of the operating system.
D. AWS manages the database type.
Answer: C
Explanation:
Explanation
RDS lowers administrative burden through automatic software patching and maintenance of the underlying operating system and secondly, you still have to manually upgrade the underlying instance type of your database cluster in order to scale it up.

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