Cloudera CCA-500 Cloudera Certified Administrator for Apache Hadoop (CCAH) Exam Practice Test

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.

In YARN, resource allocations is a function of megabytes of memory in multiples of 1024mb. Thus, they should specify the amount of memory resource they need by executing –D mapreduce-reduces.memory-mb-2048

In YARN, the ApplicationMaster is responsible for requesting the resource required for a specific launch. Thus, executing –D yarn.applicationmaster.reduce.tasks=2 will specify that the ApplicationMaster launch two task contains on the worker nodes.

Developers specify reduce tasks in the exact same way for both MapReduce version 1 (MRv1) and MapReduce version 2 (MRv2) on YARN. Thus, executing –D mapreduce.job.reduces-2 will specify reduce tasks.

In YARN, resource allocation is function of virtual cores specified by the ApplicationManager making requests to the NodeManager where a reduce task is handeled by a single container (and thus 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

You cannot enforce this, since client code can always override this value

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

Set dfs.block.size to 128 M on all the worker nodes and client machines, and set the parameter to final. You do not need to set this value on the NameNode

Set dfs.block.size to 134217728 on all the worker nodes, on all client machines, and on the NameNode, and set the parameter to final

Set dfs.block.size to 134217728 on all the worker nodes and client machines, and set the parameter to final. You do not need to set this value on the NameNode

Two active NameNodes and two Standby NameNodes

One active NameNode and one Standby NameNode

Two active NameNodes and on Standby NameNode

Unlimited. HDFS High Availability (HA) is designed to overcome limitations on the number of NameNodes you can deploy

Hadoop fs –getmerge –R westUsers.txt

Hadoop fs –getemerge westUsers westUsers.txt

Hadoop fs –cp westUsers/* westUsers.txt

Hadoop fs –get westUsers westUsers.txt

yarn.application.child.java.opts=-Xsx3072m

yarn.application.child.java.opts=-Xmx3072m

mapreduce.map.java.opts=-Xms3072m

mapreduce.map.java.opts=-Xmx3072m

For a 1GB child heap size an io.sort.mb of 128 MB will always maximize memory to disk I/O

Increase the io.sort.mb to 1GB

Decrease the io.sort.mb value to 0

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.

The file will remain under-replicated until the administrator brings that node back online

The cluster will re-replicate the file the next time the system administrator reboots the NameNode daemon (as long as the file’s replication factor doesn’t fall below)

This will be immediately re-replicated and all other HDFS operations on the cluster will halt until the cluster’s replication values are resorted

The file will be re-replicated automatically after the NameNode determines it is under-replicated based on the block reports it receives from the NameNodes

The directory will appear to be empty until the entire file write is completed on the cluster

They will see the file with a ._COPYING_ extension on its name. If they view the file, they will see contents of the file up to the last completed block (as each 64MB block is written, that block becomes available)

They will see the file with a ._COPYING_ extension on its name. If they attempt to view the file, they will get a ConcurrentFileAccessException until the entire file write is completed on the cluster

They will see the file with its original name. If they attempt to view the file, they will get a ConcurrentFileAccessException until the entire file write is completed on the cluster

Sample the web server logs web servers and copy them into HDFS using curl

Ingest the server web logs into HDFS using Flume

Channel these clickstreams into Hadoop using Hadoop Streaming

Import all user clicks from your OLTP databases into Hadoop using Sqoop

Write a MapReeeduce job with the web servers for mappers and the Hadoop cluster nodes for reducers