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NEW QUESTION 1
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 that third command?
- A. The write succeeds, overwriting foo.txt in HDFS with no warning
- B. The write silently fails
- C. The file is uploaded and stored as a plain named input
- D. You get an error message telling you that input is not a directory
- E. You get a error message telling you that foo.txt already exist
- F. The file is not written to HDFS
- G. You get an error message telling you that foo.txt already exists, and asking you if you would like to overwrite
- H. You get a warning that foo.txt is being overwritten
Answer: E
NEW QUESTION 2
Which two are Features of Hadoop's rack topology?
- A. Configuration of rack awareness is accomplished using a configuration fil
- B. You cannot use a rack topology script.
- C. Even for small clusters on a single rack, configuring rack awareness will improve performance.
- D. Rack location is considered in the HDFS block placement policy
- E. HDFS is rack aware but MapReduce daemons are not
- F. Hadoop gives preference to Intra rack data transfer in order to conserve bandwidth
Answer: BC
NEW QUESTION 3
For each YARN Job, the Hadoop framework generates task log files. Where are Hadoop’s files stored?
- A. In HDFS, In the directory of the user who generates the job
- B. On the local disk of the slave node running the task
- C. Cached In the YARN container running the task, then copied into HDFS on fob completion
- D. Cached by the NodeManager managing the job containers, then written to a log directory on the NameNode
Answer: B
Explanation: Reference: http://hortonworks.com/blog/simplifying-user-logs-management-and-access-in- yarn/
NEW QUESTION 4
You are migrating a cluster from MapReduce version 1 (MRv1) to MapReduce version2 (MRv2) on YARN. To want to maintain your MRv1 TaskTracker slot capacities when you migrate. What should you do?
- A. Configure yarn.applicationmaster.resource.memory-mb and yarn.applicationmaster.cpu- vcores so that ApplicationMaster container allocations match the capacity you require.
- B. You don’t need to configure or balance these properties in YARN as YARN dynamically balances resource management capabilities on your cluster
- C. Configure yarn.nodemanager.resource.memory-mb and yarn.nodemanager.resource.cpu-vcores to match the capacity you require under YARN for each NodeManager
- D. Configure mapred.tasktracker.map.tasks.maximum and mapred.tasktracker.reduce.tasks.maximum ub yarn.site.xml to match your cluster’s configured capacity set by yarn.scheduler.minimum-allocation
Answer: C
NEW QUESTION 5
Your cluster is running MapReduce vserion 2 (MRv2) on YARN. Your ResourceManager is configured to use the FairScheduler. Now you want to configure your scheduler such that a new user on the cluster can submit jobs into their own queue application submission. Which configuration should you set?
- A. You can specify new queue name when user submits a job and new queue can be created dynamically if yarn.scheduler.fair.user-as-default-queue = false
- B. Yarn.scheduler.fair.user-as-default-queue = false and yarn.scheduler.fair.allow- undeclared-people = true
- C. You can specify new queue name per application in allocation.fair.allow-undeclared- people = true automatically assigned to the application queue
- D. You can specify new queue name when user submits a job and new queue can be created dynamically if the property yarn.scheduler.fair.allow-undecleared-pools = true
Answer: A
NEW QUESTION 6
You have a cluster running with the Fair Scheduler enabled. There are currently no jobs running on the cluster, and you submit a job A, so that only job A is running on the cluster. A while later, you submit Job B. now job A and Job B are running on the cluster at the same time. How will the Fair Scheduler handle these two jobs?
- A. When job A gets submitted, it consumes all the tasks slots.
- B. When job A gets submitted, it doesn’t consume all the task slots
- C. When job B gets submitted, Job A has to finish first, before job B can scheduled
- D. When job B gets submitted, it will get assigned tasks, while Job A continue to run with fewer tasks.
Answer: C
NEW QUESTION 7
Your Hadoop cluster is configured with HDFS and MapReduce version 2 (MRv2) on YARN. Can you configure a worker node to run a NodeManager daemon but not a DataNode daemon and still have a function cluster?
- A. Ye
- B. The daemon will receive data from the NameNode to run Map tasks
- C. Ye
- D. The daemon will get data from another (non-local) DataNode to run Map tasks
- E. Ye
- F. The daemon will receive Reduce tasks only
Answer: A
NEW QUESTION 8
A user comes to you, complaining that when she attempts to submit a Hadoop job, it fails. There is a directory in HDFS named /data/input. The Jar is named j.jar, and the driver class is named DriverClass. She runs command:
hadoop jar j.jar DriverClass /data/input/data/output The error message returned includes the line:
PrivilegedActionException as:training (auth:SIMPLE) cause.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exits: file :/data/input
What is the cause of the error?
- A. The Hadoop configuration files on the client do not point to the cluster
- B. The directory name is misspelled in HDFS
- C. The name of the driver has been spelled incorrectly on the command line
- D. The output directory already exists
- E. The user is not authorized to run the job on the cluster
Answer: A
NEW QUESTION 9
Your cluster is configured with HDFS and MapReduce version 2 (MRv2) on YARN. What is the result when you execute: hadoop jar samplejar.jar MyClass on a client machine?
- A. SampleJar.jar is sent to the ApplicationMaster which allocation a container for Sample.jar
- B. SampleJar.Jar is serialized into an XML file which is submitted to the ApplicationMaster
- C. SampleJar.Jar is sent directly to the ResourceManager
- D. SampleJar.Jar is placed in a temporary directly in HDFS
Answer: A
NEW QUESTION 10
What 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.
- A. You must modify the configuration file on each of the six DataNode machines.
- B. You must restart the NameNode daemon to apply the changes to the cluster
- C. You must restart all six DatNode daemon to apply the changes to the cluste
- D. You don’t need to restart any daemon, as they will pick up changes automatically
- E. You must modify the configuration files on the NameNode onl
- F. DataNodes read their configuration from the master nodes.
Answer: BE
NEW QUESTION 11
Identify two features/issues that YARN is designed to address:
- A. Standardize on a single MapReduce API
- B. Single point of failure in the NameNode
- C. Reduce complexity of the MapReduce APIs
- D. Resource pressures on the JobTracker
- E. Ability to run frameworks other than MapReduce, such as MPI
- F. HDFS latency
Answer: DE
NEW QUESTION 12
You have converted your Hadoop cluster from a MapReduce 1 (MRv1) architecture to a 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 developer to implement?
- A. Developers specify reduce tasks in the exact same way for both MapReduce version 1 (MRv1) and MapReduce version 2 (MRv2) on YAR
- B. Thus, executing –p mapreduce.job.reduce-2 will specify 2 reduce tasks.
- C. In YARN, the ApplicationMaster is responsible for requesting the resources required for a specific jo
- D. Thus, executing –p yarn.applicationmaster.reduce.tasks-2 will specify that the ApplicationMaster launch two task containers on the worker nodes.
- E. In YARN, resource allocation is a function of megabytes of memory in multiple of 1024m
- F. Thus, they should specify the amount of memory resource they need by executing –D mapreduce.reduce.memory-mp-2040
- G. In YARN, resource allocation is a function of virtual cores specified by the ApplicationMaster making requests to the NodeManager where a reduce task is handled by a single container (and this 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
- H. 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.
Answer: D
NEW QUESTION 13
You decide to create a cluster which runs HDFS in High Availability mode with automatic failover, using Quorum-based Storage. What is the purpose of ZooKeeper in such a configuration?
- A. It manages the Edits file, which is a log changes to the HDFS filesystem.
- B. It monitors an NFS mount point and reports if the mount point disappears
- C. It both keeps track of which NameNode is Active at any given time, and manages the Edits file, which is a log of changes to the HDFS filesystem
- D. It only keeps track of which NameNode is Active at any given time
- E. Clients connect to ZoneKeeper to determine which NameNode is Active
Answer: D
Explanation: Reference: http://www.cloudera.com/content/cloudera-content/cloudera-docs/CDH4/latest/PDF/CDH4-High-Availability-Guide.pdf (page 15)
NEW QUESTION 14
You want to understand more about how users browse you 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 logs into your Hadoop cluster for 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. Import all users clicks from your OLTP databases into Hadoop using Sqoop
- D. Write a MApReduce job with the web servers from mappers and the Hadoop cluster nodes reducers
- E. Channel these clickstream into Hadoop using Hadoop Streaming
Answer: AB
NEW QUESTION 15
During the execution of a MapReduce v2 (MRv2) job on YARN, where does the Mapper place the intermediate data each Map task?
- A. The Mapper stores the intermediate data on the mode running the job’s ApplicationMaster so that is available to YARN’s ShuffleService before the data is presented to the Reducer
- B. The Mapper stores the intermediate data in HDFS on the node where the MAP tasks ran in the HDFS /usercache/&[user]sppcache/application_&(appid) directory for the user who ran the job
- C. YARN holds the intermediate data in the NodeManager’s memory (a container) until it is transferred to the Reducers
- D. The Mapper stores the intermediate data on the underlying filesystem of the local disk in the directories yarn.nodemanager.local-dirs
- E. The Mapper transfers the intermediate data immediately to the Reducers as it generated by the Map task
Answer: D
NEW QUESTION 16
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
- A. nn02 becomes the standby NameNode and nn01 becomes the active NameNode
- B. nn02 is fenced, and nn01 becomes the active NameNode
- C. nn01 becomes the standby NamNode and nn02 becomes the active NAmeNode
- D. nn01 is fenced, and nn02 becomes the active NameNode
Answer: D
Explanation: failover – initiate a failover between two NameNodes
This subcommand causes a failover from the first provided NameNode to the second. If the first NameNode is in the Standby state, this command simply transitions the second to the Active state without error. If the first NameNode is in the Active state, an attempt will be made to gracefully transition it to the Standby state. If this fails, the fencing methods (as configured by dfs.ha.fencing.methods) will be attempted in order until one of the methods succeeds. Only after this process will the second NameNode be transitioned to the Active state. If no fencing method succeeds, the second NameNode will not be transitioned to the Active state, and an error will be returned.
NEW QUESTION 17
On a cluster running CDH 5.0 or above, you use the hadoop fs –put command to write a
300MB file into a previously empty directory using an HDFS block of 64MB. Just after this command has finished writing 200MB of this file, what would another use see when they look in the directory?
- A. They will see the file with its original nam
- B. if they attempt to view the file, they will get a ConcurrentFileAccessException until the entire file write is completed on the cluster
- C. They will see the file with a ._COPYING_extension on its nam
- D. If they attempt to view the file, they will get a ConcurrentFileAccessException until the entire file write is completed on the cluster.
- E. They will see the file with a ._COPYING_ extension on its nam
- F. if they view the file, they will see contents of the file up to the last completed block (as each 64MB block is written, that block becomes available)
- G. The directory will appear to be empty until the entire file write is completed on the cluster
Answer: C
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