Question 1

Choose three reasons why should you run the HDFS balancer periodically? (Choose three)

Accepted Answer

A)  To ensure that there is capacity in HDFS for additional data 
B)  To ensure that all blocks in the cluster are 128MB in size 
C)  To help HDFS deliver consistent performance under heavy loads 
D)  To ensure that there is consistent disk utilization across the DataNodes 
E)  To improve data locality MapReduce

Question 2

You have installed a cluster HDFS and MapReduce version 2 (MRv2) on YARN. You have no dfs.hosts entry(ies) in your hdfs-site.xml configuration file. You configure a new worker node by setting fs.default.name in its configuration files to point to the NameNode on your cluster, and you start the DataNode daemon on that worker node. What do you have to do on the cluster to allow the worker node to join, and start sorting HDFS blocks?

Accepted Answer

A)  Without creating a dfs.hosts file or making any entries, run the commands hadoop.dfsadmin-refreshModes on the NameNode 
B)  Restart the NameNode 
C)  Creating a dfs.hosts file on the NameNode, add the worker Node's name to it, then issue the command hadoop dfsadmin -refresh Nodes = on the Namenode 
D)  Nothing; the worker node will automatically join the cluster when NameNode daemon is started

Question 3

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?

Accepted Answer

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 1024mb. Thus, they should specify the amount of memory resource they need by executing -D mapreduce-reduces.memory-mb-2048 
C)  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. 
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 -D mapreduce.job.reduces-2 will specify reduce tasks. 
E)  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 4

Which three basic configuration parameters must you set to migrate your cluster from MapReduce 1 (MRv1) to MapReduce V2 (MRv2)? (Choose three)

Accepted Answer

A) Configure the NodeManager to enable MapReduce services on YARN by setting the following property in yarn-site.xml: yarn.nodemanager.hostname your_nodeManager_shuffle B) Configure the NodeManager hostname and enable node services on YARN by setting the following property in yarn-site.xml: your_nodeManager_hostname C) Configure a default scheduler to run on YARN by setting the following property in mapred-site.xml: mapreduce.jobtracker.taskScheduler org.apache.hadoop.mapred.JobQueueTaskScheduler D) Configure the number of map tasks per jon YARN by setting the following property in mapred: mapreduce.job.maps 2 E) Configure the ResourceManager hostname and enable node services on YARN by setting the following property in yarn-site.xml: yarn.resourcemanager.hostname your_resourceManager_hostname F) Configure MapReduce as a Framework running on YARN by setting the following property in mapred-site.xml: mapreduce.framework.name yarn

Question 5

You are configuring your cluster to run HDFS and MapReducer v2 (MRv2) on YARN. Which two daemons needs to be installed on your cluster's master nodes? (Choose two)

Accepted Answer

A)  HMaster 
B)  ResourceManager 
C)  TaskManager 
D)  JobTracker 
E)  NameNode
F) DataNode

Question 6

Given:  You want to clean up this list by removing jobs where the State is KILLED. What command you enter?

Accepted Answer

A)  Yarn application -refreshJobHistory 
B)  Yarn application -kill application_1374638600275_0109 
C)  Yarn rmadmin -refreshQueue 
D)  Yarn rmadmin -kill application_1374638600275_0109

Question 7

You need to analyze 60,000,000 images stored in JPEG format, each of which is approximately 25 KB. Because you Hadoop cluster isn't optimized for storing and processing many small files, you decide to do the following actions: 1. Group the individual images into a set of larger files 2. Use the set of larger files as input for a MapReduce job that processes them directly with python using Hadoop streaming. Which data serialization system gives the flexibility to do this?

Accepted Answer

A)  CSV 
B)  XML 
C)  HTML 
D)  Avro 
E)  SequenceFiles
F) JSON

Question 8

You are running  Hadoop cluster with all monitoring facilities properly configured. Which scenario will go undeselected?

Accepted Answer

A)  HDFS is almost full 
B)  The NameNode goes down 
C)  A DataNode is disconnected from the cluster 
D)  Map or reduce tasks that are stuck in an infinite loop 
E)  MapReduce jobs are causing excessive memory swaps

Question 9

Assume you have a file named foo.txt in your local directory. You issue the following three commands: Hadoop fs -mkdir input Hadoop fs -put foo.txt input/foo.txt Hadoop fs -put foo.txt input What happens when you issue the third command?

Accepted Answer

A)  The write succeeds, overwriting foo.txt in HDFS with no warning 
B)  The file is uploaded and stored as a plain file named input 
C)  You get a warning that foo.txt is being overwritten 
D)  You get an error message telling you that foo.txt already exists, and asking you if you would like to overwrite it. 
E)  You get a error message telling you that foo.txt already exists. The file is not written to HDFS
F) You get an error message telling you that input is not a directory
G) The write silently fails

Question 10

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?

Accepted Answer

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

Exam 1: Cloudera Certified Administrator for Apache Hadoop (CCAH)

Choose three reasons why should you run the HDFS balancer periodically? (Choose three)

Which three basic configuration parameters must you set to migrate your cluster from MapReduce 1 (MRv1) to MapReduce V2 (MRv2) ? (Choose three)

You are configuring your cluster to run HDFS and MapReducer v2 (MRv2) on YARN. Which two daemons needs to be installed on your cluster's master nodes? (Choose two)

Given: 11ec3bb2_d99c_4fb7_916b_074cfa3d65b6__00 You want to clean up this list by removing jobs where the State is KILLED. What command you enter?

You are running Hadoop cluster with all monitoring facilities properly configured. Which scenario will go undeselected?

Assume you have a file named foo.txt in your local directory. You issue the following three commands: Hadoop fs -mkdir input Hadoop fs -put foo.txt input/foo.txt Hadoop fs -put foo.txt input What happens when you issue the third command?

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?