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NEW QUESTION: 1
DRAG DROP


Answer:
Explanation:

Reference: http://www.almguide.com/2014/01/deploying-an-azure-website-from-a-local-git-repo/

NEW QUESTION: 2

A. -
B. ?
C. %
D. *
Answer: A,C

NEW QUESTION: 3
MapReduce v2 (MRv2/YARN) is designed to address which two issues?
A. Single point of failure in the NameNode.
B. Resource pressure on the JobTracker.
C. Standardize on a single MapReduce API.
D. Ability to run frameworks other than MapReduce, such as MPI.
E. Reduce complexity of the MapReduce APIs.
F. HDFS latency.
Answer: B,D
Explanation:
YARN (Yet Another Resource Negotiator), as an aspect of Hadoop, has two major kinds of benefits:
*(D) The ability to use programming frameworks other than MapReduce. / MPI (Message Passing Interface) was mentioned as a paradigmatic example of a MapReduce alternative
*Scalability, no matter what programming framework you use.
Note:
*The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs.
*(B) The central goal of YARN is to clearly separate two things that are unfortunately smushed together in current Hadoop, specifically in (mainly) JobTracker:
/ Monitoring the status of the cluster with respect to which nodes have which resources available. Under YARN, this will be global. / Managing the parallelization execution of any specific job. Under YARN, this will be done separately for each job. The current Hadoop MapReduce system is fairly scalable - Yahoo runs 5000 Hadoop jobs, truly concurrently, on a single cluster, for a total 1.5 - 2 millions jobs/cluster/month. Still, YARN will remove scalability bottlenecks
Reference: Apache Hadoop YARN - Concepts & Applications

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