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Cloudera Certified Administrator for Apache Hadoop (CCAH) Questions and Answers

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Viewing questions 11-20 out of questions

Questions # 11:

You use the hadoop fs –put command to add a file “sales.txt” to HDFS. This file is small enough that it fits into a single block, which is replicated to three nodes in your cluster (with a replication factor of 3). One of the nodes holding this file (a single block) fails. How will the cluster handle the replication of file in this situation?

Options:

The file will remain under-replicated until the administrator brings that node back online

The cluster will re-replicate the file the next time the system administrator reboots the NameNode daemon (as long as the file’s replication factor doesn’t fall below)

This will be immediately re-replicated and all other HDFS operations on the cluster will halt until the cluster’s replication values are resorted

The file will be re-replicated automatically after the NameNode determines it is under-replicated based on the block reports it receives from the NameNodes

Answer

Explanation

The NameNode marks all blocks stored on the dead DataNode as under-replicated and orchestrates their rereplication. If the NameNode has marked a DataNode as dead, it will not include that DataNode in the list of machines it returns to clients as containing blocks they have requested. The NameNode will begin the process of re-replication, contacting a DataNode which contains a copy of each block which was on the now-dead DataNode and telling it to re-replicate that block to another DataNode.

Questions # 12:

Which is the default scheduler in YARN?

Options:

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

Answer

Explanation

[Reference: http://hadoop.apache.org/docs/r2.4.1/hadoop-yarn/hadoop-yarn-site/CapacityScheduler.html , , ]

Questions # 13:

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?

Options:

fstime

VERSION

Fsimage_N (where N reflects transactions up to transaction ID N)

Edits_N-M (where N-M transactions between transaction ID N and transaction ID N)

Answer

Explanation

[Reference: http://mikepluta.com/tag/namenode/ , , ]

Questions # 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?

Options:

They must be formatted as HDFS

They must be formatted as either ext3 or ext4

They may be formatted in any Linux file system

They must not be formatted - - HDFS will format the file system automatically

Answer

Questions # 15:

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?

Options:

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

Restart the NameNode and ResourceManager daemons and resubmit any running jobs.

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

Restart the NameNode of dfs.number.of.nodes in hdfs-site.xml

Answer

Explanation

http://wiki.apache.org/hadoop/FAQ#I_have_a_new_node_I_want_to_add_to_a_running_Hadoop_cluster.3B_how_do_I_start_services_on_just_one_node.3F

Questions # 16:

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

Options:

The Mapper stores the intermediate data on the node running the Job’s ApplicationMaster so that it is available to YARN ShuffleService before the data is presented to the Reducer

The Mapper stores the intermediate data in HDFS on the node where the Map tasks ran in the HDFS /usercache/&(user)/apache/application_&(appid) directory for the user who ran the job

The Mapper transfers the intermediate data immediately to the reducers as it is generated by the Map Task

YARN holds the intermediate data in the NodeManager’s memory (a container) until it is transferred to the Reducer

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

Answer

Questions # 17:

You are migrating a cluster from MApReduce version 1 (MRv1) to MapReduce version 2 (MRv2) on YARN. You want to maintain your MRv1 TaskTracker slot capacities when you migrate. What should you do/

Options:

Configure yarn.applicationmaster.resource.memory-mb and yarn.applicationmaster.resource.cpu-vcores so that ApplicationMaster container allocations match the capacity you require.

You don’t need to configure or balance these properties in YARN as YARN dynamically balances resource management capabilities on your cluster

Configure mapred.tasktracker.map.tasks.maximum and mapred.tasktracker.reduce.tasks.maximum ub yarn-site.xml to match your cluster’s capacity set by the yarn-scheduler.minimum-allocation

Configure yarn.nodemanager.resource.memory-mb and yarn.nodemanager.resource.cpu-vcores to match the capacity you require under YARN for each NodeManager

Answer

Questions # 18:

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?