CCA-505 Online Practice Questions and Answers

You have a 20 node Hadoop cluster, with 18 slave nodes and 2 master nodes running HDFS High Availability (HA). You want to minimize the chance of data loss in you cluster. What should you do?

A. Add another master node to increase the number of nodes running the JournalNode which increases the number of machines available to HA to create a quorum

B. Configure the cluster's disk drives with an appropriate fault tolerant RAID level

C. Run the ResourceManager on a different master from the NameNode in the order to load share HDFS metadata processing

D. Run a Secondary NameNode on a different master from the NameNode in order to load provide automatic recovery from a NameNode failure

E. Set an HDFS replication factor that provides data redundancy, protecting against failure

Which Yarn daemon or service monitors a Container's per-application resource usage (e.g, memory, CPU)?

A. NodeManager

B. ApplicationMaster

C. ApplicationManagerService

D. ResourceManager

Assume you have a file named foo.txt in your local directory. You issue the following three commands:

Hadoop fs mkdir input Hadoop fs put foo.txt input/foo.txt Hadoop fs put foo.txt input

What happens when you issue that third command?

A. The write succeeds, overwriting foo.txt in HDFS with no warning

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 overwritten

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.

B. 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.

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.

D. Set 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.

E. You 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

Which three basic configuration parameters must you set to migrate your cluster from MapReduce1 (MRv1) to MapReduce v2 (MRv2)?

A. Configure the NodeManager hostname and enable services on YARN by setting the following property in yarn-site.xml: yarn.nodemanager.hostname your_nodeManager_hostname

B. Configure the number of map tasks per job on YARN by setting the following property in mapredsite.xml: mapreduce.job.maps 2

C. Configure MapReduce as a framework running on YARN by setting the following property in mapredsite.xml: mapreduce.framework.name yarn

D. Configure the ResourceManager hostname and enable node services on YARN by setting the following property in yarn-site.xml: yarn.resourcemanager.hostname your_responseManager_hostname

E. Configure a default scheduler to run on YARN by setting the following property in sapred- site.xml: mapreduce.jobtracker.taskScheduler org.apache.hadoop.mapred.JobQueueTaskScheduler

F. Configure the NodeManager to enable MapReduce services on YARN by adding following property in yarn-site.xml: yarn.nodemanager.aux-services mapreduce_shuffle

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 1GB

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

Each node in your Hadoop cluster, running YARN, has 64 GB memory and 24 cores. Your yarn- site.xml

has the following configuration:

yarn.nodemanager.resource.memory-mb

32768

yarn.nodemanager.resource.cpu-vcores

23

You want YARN to launch no more than 16 containers per node. What should you do?

A. No action is needed: YARN's dynamic resource allocation automatically optimizes the node memory and cores

B. Modify yarn-site.xml with the following property: yarn.nodemanager.resource.cpu-vcores 16

C. Modify yarn-site.xml with the following property: yarn.scheduler.minimum-allocation-mb 2048

D. Modify yarn-site.xml with the following property: yarn.scheduler.minimum-allocation-mb 4096

Identify two features/issues that YARN is designed to address:

A. Standardize on a single MapReduce API

B. Single point of failure in the NameNode

C. Reduce complexity of the MapReduce APIs

D. Resource pressures on the JobTracker

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

F. HDFS latency

A user comes to you, complaining that when she attempts to submit a Hadoop job, it fails. There is a directory in HDFS named /data/input. The Jar is named j.jar, and the driver class is named DriverClass. She runs command:

hadoop jar j.jar DriverClass /data/input/data/output

The error message returned includes the line:

PrivilegedActionException as:training (auth:SIMPLE) cause.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exits: file :/data/input

What is the cause of the error?

A. The Hadoop configuration files on the client do not point to the cluster

B. The directory name is misspelled in HDFS

C. The name of the driver has been spelled incorrectly on the command line

D. The output directory already exists

E. The user is not authorized to run the job on the cluster