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NEW QUESTION: 1
バージョンは計画モデルのどこに保存されますか？ （正解をお選びください。）
A. 割り当て
B. 時間ディメンション
C. カテゴリー
D. アカウント
Answer: C

NEW QUESTION: 2
Scenario:
You have been asked to evaluate an OSPF network setup in a test lab and to answer questions a customer has about its operation. The customer has disabled your access to the show running-config command.




Areas of Router 5 and 6 are not normal areas, inspect their routing tables and determine which statement is true?
A. Only R5's loopback is present in R6's Routing table
B. R5's Loopback and R6's Loopback are both present in R5's Routing table
C. Only R6's loopback is present in R5's Routing table
D. Only R5's loopback is present in R5's Routing table
E. R5's Loopback and R6's Loopback are both present in R6's Routing table
Answer: B

NEW QUESTION: 3
An organization manages a large cloud-deployed application that employs a microservices architecture. No notable issues occur with downtime because the services of this application are redundantly deployed over three or more data center regions. However, several times a week reports are received about application slowness. The container orchestration logs show faults in a variety of containers that cause them to fail and then spin up brand new.
Which action must be taken to improve the resiliency design of the application while maintaining current scale?
A. Update the base image of the containers.
B. Add consistent "try/catch(exception)" clauses to the code.
C. Test the execution of the application with another cloud services platform.
D. Increase the number of containers running per service.
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. IdentityMapper
B. Combine
C. Speculative Execution
D. Default Partitioner
E. IdentityReducer
Answer: C
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