CCA-500 Cloudera Certified Administrator for Apache Hadoop (CCAH) exact Exam Questions

Cloudera Certified Administrator for Apache Hadoop (CCAH)

Last Update 2 hours ago Total Questions : 60

The Cloudera Certified Administrator for Apache Hadoop (CCAH) content is now fully updated, with all current exam questions added 2 hours ago. Deciding to include CCA-500 practice exam questions in your study plan goes far beyond basic test preparation.

You'll find that our CCA-500 exam questions frequently feature detailed scenarios and practical problem-solving exercises that directly mirror industry challenges. Engaging with these CCA-500 sample sets allows you to effectively manage your time and pace yourself, giving you the ability to finish any Cloudera Certified Administrator for Apache Hadoop (CCAH) practice test comfortably within the allotted time.

Question # 1

You have recently converted your Hadoop cluster from a MapReduce 1 (MRv1) architecture to 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 developers to implement?

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

Question # 2

Which scheduler would you deploy to ensure that your cluster allows short jobs to finish within a reasonable time without starting long-running jobs?

Complexity Fair Scheduler (CFS)

Capacity Scheduler

Fair Scheduler

FIFO Scheduler

Question # 3

Identify two features/issues that YARN is designated to address: (Choose two)

Standardize on a single MapReduce API

Single point of failure in the NameNode

Reduce complexity of the MapReduce APIs

Resource pressure on the JobTracker

Ability to run framework other than MapReduce, such as MPI

HDFS latency

Question # 4

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 size property to 3GB to optimize performance?

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

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

mapreduce.map.java.opts=-Xms3072m

mapreduce.map.java.opts=-Xmx3072m

Question # 5

Which is the default scheduler in YARN?

YARN doesn’t configure a default scheduler, you must first assign an appropriate scheduler class in yarn-site.xml

Capacity Scheduler

Fair Scheduler

FIFO Scheduler

Question # 6

You are running a Hadoop cluster with a NameNode on host mynamenode. What are two ways to determine available HDFS space in your cluster?

Run hdfs fs –du / and locate the DFS Remaining value

Run hdfs dfsadmin –report and locate the DFS Remaining value

Run hdfs dfs / and subtract NDFS Used from configured Capacity

Connect to http://mynamenode:50070/dfshealth.jsp and locate the DFS remaining value

Question # 7

Your Hadoop cluster contains nodes in three racks. You have not configured the dfs.hosts property in the NameNode’s configuration file. What results?

The NameNode will update the dfs.hosts property to include machines running the DataNode daemon on the next NameNode reboot or with the command dfsadmin –refreshNodes

No new nodes can be added to the cluster until you specify them in the dfs.hosts file

Any machine running the DataNode daemon can immediately join the cluster

Presented with a blank dfs.hosts property, the NameNode will permit DataNodes specified in mapred.hosts to join the cluster

Question # 8

Which YARN daemon or service negotiations map and reduce Containers from the Scheduler, tracking their status and monitoring progress?

NodeManager

ApplicationMaster

ApplicationManager

ResourceManager

Question # 9

Your cluster implements HDFS High Availability (HA). Your two NameNodes are named nn01 and nn02. What occurs when you execute the command: hdfs haadmin –failover nn01 nn02?

nn02 is fenced, and nn01 becomes the active NameNode

nn01 is fenced, and nn02 becomes the active NameNode

nn01 becomes the standby NameNode and nn02 becomes the active NameNode

nn02 becomes the standby NameNode and nn01 becomes the active NameNode

Question # 10

What two processes must you do if you are running a Hadoop cluster with a single NameNode and six DataNodes, and you want to change a configuration parameter so that it affects all six DataNodes. (Choose two)

You must modify the configuration files on the NameNode only. DataNodes read their configuration from the master nodes

You must modify the configuration files on each of the DataNodes machines

You don’t need to restart any daemon, as they will pick up changes automatically

You must restart the NameNode daemon to apply the changes to the cluster

You must restart all six DatNode daemon to apply the changes to the cluster