Cloudera Hadoop Administrator Certification Certification Questions and Answer (Dumps and Practice Questions)

Question :

The ApplicationMaster has to emit __________ to the ResourceManager to keep it informed that the ApplicationMaster is alive and still running.




1. heartbeats
2. messages
3. events
4. Async Messages


Correct Answer : 1


Explanation: The ApplicationMaster has to emit heartbeats to the ResourceManager to keep it informed that the ApplicationMaster is alive and still running. The timeout expiry interval at the ResourceManager is defined by a config setting accessible via YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS with the default being defined by YarnConfiguration.DEFAULT_RM_AM_EXPIRY_INTERVAL_MS. The AMRMProtocol#allocate calls to the ResourceManager count as heartbeats as it also supports sending progress update information. Therefore, an allocate call with no containers requested and progress information updated if any is a valid way for making heartbeat calls to the ResourceManager.






Question :
A _____ is the basic unit of processing capacity in YARN, and is an encapsulation of resource elements (memory, cpu etc.)


1. Node Manager
2. Container
3. ApplicationMaster
4. DataNode

Correct Answer : 2
A Container is the basic unit of processing capacity in YARN, and is an encapsulation of resource elements (memory, cpu etc.).





Question :

Select the correct option which is/are correct



1. YARN takes into account all of the available compute resources on each machine in the cluster.
2. Based on the available resources, YARN negotiates resource requests from applications (such as MapReduce) running in the cluster.
3. YARN then provides processing capacity to each application by allocating Containers.
4. 1 and 3
5. 1,2 and 3

Correct Answer : 5

YARN takes into account all of the available compute resources on each machine in the cluster. Based on the available resources, YARN negotiates resource requests from applications (such as MapReduce) running in the cluster. YARN then provides processing capacity to each application by allocating Containers. A Container is the basic unit of processing capacity in YARN, and is an encapsulation of resource elements (memory, cpu etc.).

Related Questions

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Question :

Select the correct statement which applies to "Fair Scheduler"



1. Fair Scheduler allows assigning guaranteed minimum shares to queues
2. queue does not need its full guaranteed share, the excess will not be splitted between other running apps.
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4. 1 and 3
5. 1,2 and 3


Question : In fair scheduler you have defined a Hierarchical queue named QueueC, whose parent is QueueB and QueueB's parent is QueueA. Which is the
correct name format to reffer the QueueB

1. root.QueueB
2. QueueC.QueueB
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4. leaf.QueueC.QueueB


Question : To use the Fair Scheduler first assign the appropriate scheduler class in yarn-site.xml:

Select the correct value which can be placed in the name field.


1. yarn.resourcemanager.scheduler
2. yarn.resourcemanager.class
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4. yarn.scheduler.class


Question : You have a setup of YARN cluster where the total application memory available is GB, there are department queues,
IT and DCS. The IT queue has 15 GB allocated and DCS queue has 5 GB allocated. Each map task requires 10 GB allocation.
How does the FairScheduler assign the available memory resources under the Single Resource Fairness(SRF) rule?

1. DCS has less resources and will be granted the next 10 GB that becomes available
2. None of them will be granted any memory as both queues have allocated memory
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4. IT has more resources and will be granted the next 10 GB that becomes available


Question : Your Data Management Hadoop cluster has a total of GB of memory capacity.
Researcher Allen submits a MapReduce Job Equity which is configured to require a total of 100 GB of memory and few seconds later,
Researcher Babita submits a MapReduce Job ETF which is configured to require a total of 15 GB of memory. Using the YARN FairScheduler,
do the tasks in Job Equity have to finish before the tasks in Job ETF can start?


1. The tasks in Job ETF have to wait until the tasks in Job Equity to finish as there is no memory left
2. The tasks in Job ETF do not have to wait for the tasks in Job Equity to finish, tasks in Job ETF can start when resources become available
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4. The tasks in Job Equity have to wait until the tasks in Job ETF to finish as small jobs will be given priority


Question : Select the two correct statements from below regarding the Hadoop Cluster Infrastructure
configured with Fair Scheduler and each MapReduce ETL work will be assigned to a pool.

1. Pools are assigned priorities. Pools with higher priorities are executed before pools with lower priorities.
2. Each pool's share of task slots remains static during the execution of any individual job.

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4. Each pool's share of task slots may change throughout the course of job execution.

5. Each pool gets exactly 1/N of the total available task slots, where N is the number of jobs running on the cluster.
6. Pools get a dynamically-allocated share of the available task slots (subject to additional constraints)


1. 1,4,6
2. 3,4,6
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4. 2,3,4
5. 4,5,6