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Free demo questions for Cloudera CCA-500 Exam Dumps Below:

NEW QUESTION 1
You are planning a Hadoop cluster and considering implementing 10 Gigabit Ethernet as the network fabric. Which workloads benefit the most from faster network fabric?

  • A. When your workload generates a large amount of output data, significantly larger than the amount of intermediate data
  • B. When your workload consumes a large amount of input data, relative to the entire capacity if HDFS
  • C. When your workload consists of processor-intensive tasks
  • D. When your workload generates a large amount of intermediate data, on the order of the input data itself

Answer: A

NEW QUESTION 2
In CDH4 and later, which file contains a serialized form of all the directory and files inodes in the filesystem, giving the NameNode a persistent checkpoint of the filesystem metadata?

  • A. fstime
  • B. VERSION
  • C. Fsimage_N (where N reflects transactions up to transaction ID N)
  • D. Edits_N-M (where N-M transactions between transaction ID N and transaction ID N)

Answer: C

Explanation: Reference:http://mikepluta.com/tag/namenode/

NEW QUESTION 3
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 your 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. Set an HDFS replication factor that provides data redundancy, protecting against node failure
  • C. Run a Secondary NameNode on a different master from the NameNode in order to provide automatic recovery from a NameNode failure.
  • D. Run the ResourceManager on a different master from the NameNode in order to load- share HDFS metadata processing
  • E. Configure the cluster’s disk drives with an appropriate fault tolerant RAID level

Answer: D

NEW QUESTION 4
You want to understand more about how users browse your public website. For example, you want to know which pages they visit prior to placing an order. You have a server farm of 200 web servers hosting your website. Which is the most efficient process to gather these web server across logs into your Hadoop cluster analysis?

  • A. Sample the web server logs web servers and copy them into HDFS using curl
  • B. Ingest the server web logs into HDFS using Flume
  • C. Channel these clickstreams into Hadoop using Hadoop Streaming
  • D. Import all user clicks from your OLTP databases into Hadoop using Sqoop
  • E. Write a MapReeeduce job with the web servers for mappers and the Hadoop cluster nodes for reducers

Answer: B

Explanation: Apache Flume is a service for streaming logs into Hadoop.
Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming data into the Hadoop Distributed File System (HDFS). It has a simple and flexible architecture based on streaming data flows; and is robust and fault tolerant with tunable reliability mechanisms for failover and recovery.

NEW QUESTION 5
Which command does Hadoop offer to discover missing or corrupt HDFS data?

  • A. Hdfs fs –du
  • B. Hdfs fsck
  • C. Dskchk
  • D. The map-only checksum
  • E. Hadoop does not provide any tools to discover missing or corrupt data; there is not need because three replicas are kept for each data block

Answer: B

Explanation: Reference:https://twiki.grid.iu.edu/bin/view/Storage/HadoopRecovery

NEW QUESTION 6
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?

  • 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 allocations is a function of megabytes of memory in multiples of 1024m
  • C. Thus, they should specify the amount of memory resource they need by executing –D mapreduce-reduces.memory-mb-2048
  • D. In YARN, the ApplicationMaster is responsible for requesting the resource required for a specific launc
  • E. Thus, executing –D yarn.applicationmaster.reduce.tasks=2 will specify that the ApplicationMaster launch two task contains on the worker nodes.
  • F. Developers specify reduce tasks in the exact same way for both MapReduce version 1 (MRv1) and MapReduce version 2 (MRv2) on YAR
  • G. Thus, executing –D mapreduce.job.reduces-2 will specify reduce tasks.
  • H. 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

Answer: D

NEW QUESTION 7
Cluster Summary:
45 files and directories, 12 blocks = 57 total. Heap size is 15.31 MB/193.38MB(7%)
CCA-500 dumps exhibit
Refer to the above screenshot.
You configure a Hadoop cluster with seven DataNodes and on of your monitoring UIs displays the details shown in the exhibit.
What does the this tell you?

  • A. The DataNode JVM on one host is not active
  • B. Because your under-replicated blocks count matches the Live Nodes, one node is dead, and your DFS Used % equals 0%, you can’t be certain that your cluster has all the data you’ve written it.
  • C. Your cluster has lost all HDFS data which had bocks stored on the dead DatNode
  • D. The HDFS cluster is in safe mode

Answer: A

NEW QUESTION 8
You want to node to only swap Hadoop daemon data from RAM to disk when absolutely necessary. What should you do?

  • A. Delete the /dev/vmswap file on the node
  • B. Delete the /etc/swap file on the node
  • C. Set the ram.swap parameter to 0 in core-site.xml
  • D. Set vm.swapfile file on the node
  • E. Delete the /swapfile file on the node

Answer: D

NEW QUESTION 9
You have a cluster running with a FIFO scheduler enabled. You submit a large job A to the cluster, which you expect to run for one hour. Then, you submit job B to the cluster, which you expect to run a couple of minutes only.
You submit both jobs with the same priority.
Which two best describes how FIFO Scheduler arbitrates the cluster resources for job and its tasks?(Choose two)

  • A. Because there is a more than a single job on the cluster, the FIFO Scheduler will enforce a limit on the percentage of resources allocated to a particular job at any given time
  • B. Tasks are scheduled on the order of their job submission
  • C. The order of execution of job may vary
  • D. Given job A and submitted in that order, all tasks from job A are guaranteed to finish before all tasks from job B
  • E. The FIFO Scheduler will give, on average, and equal share of the cluster resources over the job lifecycle
  • F. The FIFO Scheduler will pass an exception back to the client when Job B is submitted, since all slots on the cluster are use

Answer: AD

NEW QUESTION 10
What does CDH packaging do on install to facilitate Kerberos security setup?

  • A. Automatically configures permissions for log files at & MAPRED_LOG_DIR/userlogs
  • B. Creates users for hdfs and mapreduce to facilitate role assignment
  • C. Creates directories for temp, hdfs, and mapreduce with the correct permissions
  • D. Creates a set of pre-configured Kerberos keytab files and their permissions
  • E. Creates and configures your kdc with default cluster values

Answer: B

NEW QUESTION 11
Which two are features of Hadoop’s rack topology?(Choose two)

  • A. Configuration of rack awareness is accomplished using a configuration fil
  • B. You cannot use a rack topology script.
  • C. Hadoop gives preference to intra-rack data transfer in order to conserve bandwidth
  • D. Rack location is considered in the HDFS block placement policy
  • E. HDFS is rack aware but MapReduce daemon are not
  • F. Even for small clusters on a single rack, configuring rack awareness will improve performance

Answer: BC

NEW QUESTION 12
Table schemas in Hive are:

  • A. Stored as metadata on the NameNode
  • B. Stored along with the data in HDFS
  • C. Stored in the Metadata
  • D. Stored in ZooKeeper

Answer: B

NEW QUESTION 13
You’re 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 version 1 (MRv1) to one running HDFS and MapReduce version 2 (MRv2) on YARN. You want to set and enforce a block size of 128MB for all new files written to the cluster after upgrade. What should you do?

  • A. You cannot enforce this, since client code can always override this value
  • B. 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
  • C. Set dfs.block.size to 128 M on all the worker nodes and client machines, and set the parameter to fina
  • D. You do not need to set this value on the NameNode
  • E. 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
  • F. Set dfs.block.size to 134217728 on all the worker nodes and client machines, and set the parameter to fina
  • G. You do not need to set this value on the NameNode

Answer: C

NEW QUESTION 14
You are configuring a server running HDFS, MapReduce version 2 (MRv2) on YARN running Linux. How must you format underlying file system of each DataNode?

  • A. They must be formatted as HDFS
  • B. They must be formatted as either ext3 or ext4
  • C. They may be formatted in any Linux file system
  • D. They must not be formatted - - HDFS will format the file system automatically

Answer: B

NEW QUESTION 15
Your cluster is configured with HDFS and MapReduce version 2 (MRv2) on YARN. What is the result when you execute: hadoop jar SampleJar MyClass on a client machine?

  • A. SampleJar.Jar is sent to the ApplicationMaster which allocates a container for SampleJar.Jar
  • B. Sample.jar is placed in a temporary directory in HDFS
  • C. SampleJar.jar is sent directly to the ResourceManager
  • D. SampleJar.jar is serialized into an XML file which is submitted to the ApplicatoionMaster

Answer: A

NEW QUESTION 16
Which YARN daemon or service negotiations map and reduce Containers from the Scheduler, tracking their status and monitoring progress?

  • A. NodeManager
  • B. ApplicationMaster
  • C. ApplicationManager
  • D. ResourceManager

Answer: B

Explanation: Reference:http://www.devx.com/opensource/intro-to-apache-mapreduce-2-yarn.html(See resource manager)

NEW QUESTION 17
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 cluster?

  • A. Nothing, other than ensuring that the DNS (or/etc/hosts files on all machines) contains any entry for the new node.
  • B. Restart the NameNode and ResourceManager daemons and resubmit any running jobs.
  • C. Add a new entry to /etc/nodes on the NameNode host.
  • D. Restart the NameNode of dfs.number.of.nodes in hdfs-site.xml

Answer: A

Explanation: http://wiki.apache.org/hadoop/FAQ#I_have_a_new_node_I_want_to_add_to_a_running_H adoop_cluster.3B_how_do_I_start_services_on_just_one_node.3F

NEW QUESTION 18
Which scheduler would you deploy to ensure that your cluster allows short jobs to finish within a reasonable time without starting long-running jobs?

  • A. Complexity Fair Scheduler (CFS)
  • B. Capacity Scheduler
  • C. Fair Scheduler
  • D. FIFO Scheduler

Answer: C

Explanation: Reference:http://hadoop.apache.org/docs/r1.2.1/fair_scheduler.html

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