Your cluster's mapred-start.xml includes the following parameters

mapreduce.map.memory.mb 4096 mapreduce.reduce.memory.mb 8192

And any cluster's yarn-site.xml includes the following parameters

yarn.nodemanager.vmen-pmen-ration 2.1

What is the maximum amount of virtual memory allocated for each map task before YARN will kill its Container?

A. 4 GB

B. 17.2 GB

C. 8.9 GB

D. 8.2 GB

E. 24.6 GB

Assuming you're not running HDFS Federation, what is the maximum number of NameNode daemons you should run on your cluster in order to avoid a "split-brain" scenario with your NameNode when running HDFS High Availability (HA) using Quorum-based storage?

A. Two active NameNodes and two Standby NameNodes

B. One active NameNode and one Standby NameNode

C. Two active NameNodes and on Standby NameNode

D. Unlimited. HDFS High Availability (HA) is designed to overcome limitations on the number of NameNodes you can deploy

For each YARN job, the Hadoop framework generates task log file. Where are Hadoop task log files stored?

A. Cached by the NodeManager managing the job containers, then written to a log directory on the NameNode

B. Cached in the YARN container running the task, then copied into HDFS on job completion

C. In HDFS, in the directory of the user who generates the job

D. On the local disk of the slave mode running the task

You observed that the number of spilled records from Map tasks far exceeds the number of map output records. Your child heap size is 1GB and your io.sort.mb value is set to 1000MB. How would you tune your io.sort.mb value to achieve maximum memory to disk I/O ratio?

A. For a 1GB child heap size an io.sort.mb of 128 MB will always maximize memory to disk I/O

B. Increase the io.sort.mb to 1GB

C. Decrease the io.sort.mb value to 0

D. Tune the io.sort.mb value until you observe that the number of spilled records equals (or is as close to equals) the number of map output records.

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?

A. CSV

B. XML

C. HTML

D. Avro

E. SequenceFiles

F. JSON

Which is the default scheduler in YARN?

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

B. Capacity Scheduler

C. Fair Scheduler

D. FIFO Scheduler

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

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 final. You do not need to set this value on the NameNode

D. 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

E. Set dfs.block.size to 134217728 on all the worker nodes and client machines, and set the parameter to final. You do not need to set this value on the NameNode

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

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?

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

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

C. mapreduce.map.java.opts=-Xms3072m

D. mapreduce.map.java.opts=-Xmx3072m