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. 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.
• B. 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
• C. 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.cpuvcores=2
• D. 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.
• E. In 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 nodes.

You want a node to only swap Hadoop daemon data from RAM to disk when absolutely necessary. What should you do?

• A. Delete the /swapfile file on the node
• B. Set vm.swappiness to o in /etc/sysctl.conf
• C. Set the ram.swap parameter to o in core-site.xml
• D. Delete the /dev/vmswap file on the node
• E. Delete the /etc/swap file on the node

You are migrating a cluster from MapReduce version 1 (MRv1) to MapReduce version2 (MRv2) on YARN. To want to maintain your MRv1 TaskTracker slot capacities when you migrate. What should you do?

• A. Configure yarn.nodemanager.resource.memory-mb and yarn.nodemanager.resource.cpu-vcores to match the capacity you require under YARN for each NodeManager
• B. Configure yarn.applicationmaster.resource.memory-mb and yarn.applicationmaster.cpuvcores so that ApplicationMaster container allocations match the capacity you require.
• C. Configure mapred.tasktracker.map.tasks.maximum and mapred.tasktracker.reduce.tasks.maximum ub yarn.site.xml to match your cluster's configured capacity set by yarn.scheduler.minimum-allocation
• D. You don't need to configure or balance these properties in YARN as YARN dynamically balances resource management capabilities on your cluster

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=-3072m
• B. Yarn.application.child.java.opts-Xax3072m
• C. Mapreduce.map.java.opts=-Xms3072m
• D. Mapreduce.map.java.opts=-Xmx3072m

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

• A. Ability to run frameworks other than MapReduce, such as MPI
• B. Reduce complexity of the MapReduce APIs
• C. Standardize on a single MapReduce API
• D. HDFS latency
• E. Resource pressures on the JobTracker
• F. Single point of failure in the NameNode