Identify two features/issues that MapReduce v2 (MRv2/YARN) is designed to address:

A.
Resource pressure on the JobTrackr

B.
HDFS latency.

C.
Ability to run frameworks other than MapReduce, such as MPI.

D.
Reduce complexity of the MapReduce APIs.

E.
Single point of failure in the NameNode.

F.
Standardize on a single MapReduce API.

Explanation:
A: MapReduce has undergone a complete overhaul in hadoop-0.23 and we now
have, what we call, MapReduce 2.0 (MRv2) or YARN.
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.
The ResourceManager and per-node slave, the NodeManager (NM), form the data-computation
framework. The ResourceManager is the ultimate authority that arbitrates resources among all the
applications in the system.
The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with
negotiating resources from the ResourceManager and working with the NodeManager(s) to
execute and monitor the tasks.
C: YARN, as an aspect of Hadoop, has two major kinds of benefits:
The ability to use programming frameworks other than MapReduce.
Scalability, no matter what programming framework you use.