CCA-505 Online Practice Questions and Answers

Assuming a cluster running HDFS, MapReduce version 2 (MRv2) on YARN with all settings at their default, what do you need to do when adding a new slave node to a cluster?

A. Nothing, other than ensuring that DNS (or /etc/hosts files on all machines) contains am entry for the new node.

B. Restart the NameNode and ResourceManager deamons and resubmit any running jobs

C. Increase the value of dfs.number.of.needs in hdfs-site.xml

D. Add a new entry to /etc/nodes on the NameNode host.

E. Restart the NameNode daemon.

During the execution of a MapReduce v2 (MRv2) job on YARN, where does the Mapper place the intermediate data each Map task?

A. The Mapper stores the intermediate data on the mode running the job's ApplicationMaster so that is available to YARN's ShuffleService before the data is presented to the Reducer

B. The Mapper stores the intermediate data in HDFS on the node where the MAP tasks ran in the HDFS / usercache/and[user]sppcache/application_and(appid) directory for the user who ran the job

C. YARN holds the intermediate data in the NodeManager's memory (a container) until it is transferred to the Reducers

D. The Mapper stores the intermediate data on the underlying filesystem of the local disk in the directories yarn.nodemanager.local-dirs

E. The Mapper transfers the intermediate data immediately to the Reducers as it generated by the Map task

Your cluster's mapped-site.xml includes the following parameters

mapreduce.map.memory.mb

4096

mapreduce.reduce.memory,mb

8192

And your cluster's yarn-site.xml includes the following parameters

yarn.nodemanager/vmen-pmem-ratio

2.1

What is the maximum amount of virtual memory allocated for each map before YARN will kill its Container?

A. 4 GB

B. 17.2 GB

C. 24.6 GB

D. 8.2 GB

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 are running a Hadoop cluster with MapReduce version 2 (MRv2) on YARN. You consistently see that MapReduce map tasks on your cluster are running slowly because of excessive garbage collection of JVM, how do you increase JVM heap property to 3GB to optimize performance?

A. Yarn.application.child.java.opts-Xax3072m

B. Yarn.application.child.java.opts=-3072m

C. Mapreduce.map.java.opts=-Xmx3072m

D. Mapreduce.map.java.opts=-Xms3072m

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

C. When job B gets submitted, Job A has to finish first, before job B can scheduled

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 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

Which is the default scheduler in YARN?

A. Fair Scheduler

B. FIFO Scheduler

C. Capacity Scheduler

D. YARN doesn't configure a default scheduler. You must first assign a appropriate scheduler class in yarn-site.xml

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