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You are configuring your cluster to run HDFS and MapReduce v2
(MRv2) on YARN.
Which daemons need to be installed on your clusters master
Assume you have a file named foo.txt in your local directory.
You issue the following three commands:
Hadoop fs -mkdir input
fs -put foo.txt input/foo.txt
Hadoop fs -put foo.txt input
when you issue that third command?
A. The write succeeds, overwriting foo.txt in HDFS with no
B. The write silently fails
C. The file is uploaded and
stored as a plain named input
D. You get an error message telling you that
input is not a directory
E. You get a error message telling you that
foo.txt already exists. The file is not written to HDFS
F. You get an
error message telling you that foo.txt already exists, and asking you if you
would like to overwrite
G. You get a warning that foo.txt is being
You have a Hadoop cluster running HDFS, and a gateway machine
external to the cluster from which clients submit jobs. What do you need to do
in order to run on the cluster and submit jobs from the command line of the
A. Install the impslad daemon, statestored daemon, and catalogd daemon on
each machine in the cluster and on the gateway node
B. Install the impalad
daemon on each machine in the cluster, the statestored daemon and catalogd
daemon on one machine in the cluster, and the impala shell on your gateway
C. Install the impalad daemon and the impala shell on your gateway
machine, and the statestored daemon and catalog daemon on one of the nodes in
D. Install the impalad daemon, the statestored daemon, the
catalogd daemon, and the impala shell on your gateway machine
the impalad daemon, statestored daemon, and catalogd daemon on each machine in
the cluster, and the impala shell on your gateway machine
You have converted your Hadoop cluster from a MapReduce 1
(MRv1) architecture to a MapReduce 2 (MRv2) on YARN architecture. Your
developers are accustomed to specifying map and reduce tasks (resource
allocation) tasks when they run jobs. A developer wants to know how specify to
reduce tasks when a specific job runs.
Which method should you tell that
developer to implement?
A. Developers specify reduce tasks in the exact same way for both
MapReduce version 1 (MRv1) and MapReduce version 2 (MRv2) on YARN. Thus,
executing -p mapreduce.job.reduce-2 will specify 2 reduce tasks.
YARN, the ApplicationMaster is responsible for requesting the resources required
for a specific job. Thus, executing -p yarn.applicationmaster.reduce.tasks-2
will specify that the ApplicationMaster launch two task containers on the worker
C. In YARN, resource allocation is a function of megabytes of
memory in multiple of 1024mb.
Thus, they should specify the amount of memory
resource they need by executing -D mapreduce.reduce.memory-mp-2040
D. In YARN, resource allocation is a function of virtual cores specified
by the ApplicationMaster making requests to the NodeManager where a reduce task
is handled by a single container (and this a single virtual core). Thus, the
developer needs to specify the number of virtual cores to the NodeManager by
executing -p yarn.nodemanager.cpu-vcores=2
E. MapReduce version 2 (MRv2) on YARN abstracts resource allocation away
from the idea of “tasks” into memory and virtual cores, thus eliminating the
need for a developer to specify the number of reduce tasks, and indeed
preventing the developer from specifying the number of reduce tasks.
You are upgrading a Hadoop cluster from HDFS and MapReduce
version 1 (MRv1) to one running HDFS and MapReduce version 2 (MRv2) on YARN. You
want to set and enforce a block of 128MB for all new files written to the
cluster after the upgrade. What should you do?
A. Set dfs.block.size to 128M on all the worker nodes, on all client
machines, and on the NameNode, and set the parameter to final.
dfs.block.size to 134217728 on all the worker nodes, on all client machines, and
on the NameNode, and set the parameter to final.
C. Set dfs.block.size to
134217728 on all the worker nodes and client machines, and set the parameter to
final. You do need to set this value on the NameNode.
dfs.block.size to 128M on all the worker nodes and client machines, and set the
parameter to final. You do need to set this value on the NameNode.
cannot enforce this, since client code can always override this value.
Which two are Features of Hadoop’s rack topology?
A. Configuration of rack awareness is accomplished using a configuration
file. You cannot use a rack topology script.
B. Even for small clusters on
a single rack, configuring rack awareness will improve performance.
C. Rack location is considered in the HDFS block placement policy
D. HDFS is
rack aware but MapReduce daemons are not
E. Hadoop gives preference to
Intra rack data transfer in order to conserve bandwidth
You want to understand more about how users browse you public
website. For example, you want to know which pages they visit prior to placing
an order. You have a server farm of 200 web servers hosting your website. Which
is the most efficient process to gather these web server logs into your Hadoop
cluster for analysis?
A. Sample the web server logs web servers and copy them into HDFS using
B. Ingest the server web logs into HDFS using Flume
all users clicks from your OLTP databases into Hadoop using Sqoop
a MApReduce job with the web servers from mappers and the Hadoop cluster nodes
E. Channel these clickstream into Hadoop using Hadoop
Your cluster implements HDFS High Availability (HA). Your two
NameNodes are named nn01 and nn02. What occurs when you execute the command:
hdfs haadmin -failover nn01 nn02
A. nn02 becomes the standby NameNode and nn01 becomes the active
B. nn02 is fenced, and nn01 becomes the active NameNode
C. nn01 becomes the standby NamNode and nn02 becomes the active NAmeNode
D. nn01 is fenced, and nn02 becomes the active NameNode
failover – initiate a failover between two
NameNodes This subcommand causes a failover from the first provided NameNode to
the second. If the first NameNode is in the Standby state, this command simply
transitions the second to the Active state without error. If the first NameNode
is in the Active state, an attempt will be made to gracefully transition it to
the Standby state. If this fails, the fencing methods (as configured by
dfs.ha.fencing.methods) will be attempted in order until one of the methods
succeeds. Only after this process will the second NameNode be transitioned to
the Active state. If no fencing method succeeds, the second NameNode will not be
transitioned to the Active state, and an error will be returned.
Your Hadoop cluster is configured with HDFS and MapReduce
version 2 (MRv2) on YARN. Can you configure a worker node to run a NodeManager
daemon but not a DataNode daemon and still have a function cluster?
A. Yes. The daemon will receive data from the NameNode to run Map
B. Yes. The daemon will get data from another (non-local) DataNode
to run Map tasks
C. Yes. The daemon will receive Reduce tasks only
Which YARN process runs as “controller O” of a submitted job
and is responsible for resource requests?
You have a cluster running with the Fair Scheduler enabled.
There are currently no jobs running on the cluster, and you submit a job A, so
that only job A is running on the cluster. A while later, you submit Job B. now
job A and Job B are running on the cluster at the same time. How will the Fair
Scheduler handle these two jobs?
A. When job A gets submitted, it consumes all the tasks slots.
B. When job A gets submitted, it doesn’t consume all the task slots
job B gets submitted, Job A has to finish first, before job B can
D. When job B gets submitted, it will get assigned tasks, while
Job A continue to run with fewer tasks.
You observe that the number of spilled records from Map tasks
far exceeds the number of map output records. Your child heap size is 1GB and
your io.sort.mb value is set to 100 MB. How would you tune your io.sort.mb value
to achieve maximum memory to disk I/O ratio?
A. Decrease the io.sort.mb value to 0
B. Increase the io.sort.mb to
C. For 1GB child heap size an io.sort.mb of 128 MB will always
maximize memory to disk I/O
D. Tune the io.sort.mb value until you observe
that the number of spilled records equals (or is as close to equals) the number
of map output records
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