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CCD-470 Examination questions (September)

Achieve New Updated (September) Cloudera CCD-470 Examination Questions 21-30

September 24, 2015

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QUESTION 21

You need to move a file titled “weblogs” into HDFS. When you try to copy the file, you can’t. You know you have ample space on your DataNodes. Which action should you take to relieve this situation and store more files in HDFS?

 

A.

Increase the block size on all current files in HDFS.

 

 

 

 

B.

Increase the block size on your remaining files.

C.

Decrease the block size on your remaining files.

D.

Increase the amount of memory for the NameNode.

E.

Increase the number of disks (or size) for the NameNode.

F.

Decrease the block size on all current files in HDFS.

 

Answer: C

Explanation:

Note:

* -put localSrc destCopies the file or directory from the local file system identified by localSrc to dest within the DFS.

* What is HDFS Block size? How is it different from traditional file system block size?

 

In HDFS data is split into blocks and distributed across multiple nodes in the cluster. Each block is typically 64Mb or 128Mb in size. Each block is replicated multiple times. Default is to replicate each block three times. Replicas are stored on different nodes. HDFS utilizes the local file system to store each HDFS block as a separate file. HDFS Block size can not be compared with the traditional file system block size.

 

 

QUESTION 22

If you run the word count MapReduce program with m mappers and r reducers, how many output files will you get at the end of the job? And how many key-value pairs will there be in each file? Assume k is the number of unique words in the input files.

 

A.

There will be r files, each with exactly k/r key-value pairs.

B.

There will be r files, each with approximately k/m key-value pairs.

C.

There will be r files, each with approximately k/r key-value pairs.

D.

There will be m files, each with exactly k/m key value pairs.

E.

There will be m files, each with approximately k/m key-value pairs.

 

Answer: A

Explanation:Note:

* A MapReduce job with m mappers and r reducers involves up to m * r distinct copy operations, since each mapper may have intermediate output going to every reducer.

* In the canonical example of word counting, a key-value pair is emitted for every word found. For example, if we had 1,000 words, then 1,000 key-value pairs will be emitted from

 

 

 

 

 

the mappers to the reducer(s).

 

 

QUESTION 23

You are running a job that will process a single InputSplit on a cluster which has no other jobs currently running. Each node has an equal number of open Map slots. On which node will Hadoop first attempt to run the Map task?

 

A.

The node with the most memory

B.

The node with the lowest system load

C.

The node on which this InputSplit is stored

D.

The node with the most free local disk space

 

Answer: C

Explanation: The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster workcan be delegated. When the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.

 

 

QUESTION 24

When is the earliest point at which the reduce method of a given Reducer can be called?

 

A.

As soon as at least one mapper has finished processing its input split.

B.

As soon as a mapper has emitted at least one record.

C.

Not until all mappers have finished processing all records.

D.

It depends on the InputFormat used for the job.

 

Answer: C

Explanation: In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from

 

 

 

 

 

the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished.

 

Note: The reduce phase has 3 steps: shuffle, sort, reduce. Shuffle is where the data is collected by the reducer from each mapper. This can happen while mappers are generating data since it is only a data transfer. On the other hand, sort and reduce can only start once all the mappers are done.

 

Why is starting the reducers early a good thing? Because it spreads out the data transfer from the mappers to the reducers over time, which is a good thing if your network is the bottleneck.

 

Why is starting the reducers early a bad thing? Because they “hog up” reduce slots while only copying data. Another job that starts later that will actually use the reduce slots now can’t use them.

 

You can customize when the reducers startup by changing the default value of mapred.reduce.slowstart.completed.maps in mapred-site.xml. A value of 1.00 will wait for all the mappers to finish before starting the reducers. A value of 0.0 will start the reducers right away. A value of 0.5 will start the reducers when half of the mappers are complete. You can also change mapred.reduce.slowstart.completed.maps on a job-by-job basis.

 

Typically, keep mapred.reduce.slowstart.completed.maps above 0.9 if the system ever has multiple jobs running at once. This way the job doesn’t hog up reducers when they aren’t doing anything but copying data. If you only ever have one job running at a time, doing 0.1 would probably be appropriate.

 

Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, When is the reducers are started in a MapReduce job?

 

 

QUESTION 25

Assuming default settings, which best describes the order of data provided to a reducer’s reduce method:

 

A.

The keys given to a reducer aren’t in a predictable order, but the values associated with those keys always are.

 

 

 

 

B.

Both the keys and values passed to a reducer always appear in sorted order.

C.

Neither keys nor values are in any predictable order.

D.

The keys given to a reducer are in sorted order but the values associated with each key are in no predictable order

 

Answer: D

Explanation: Reducer has 3 primary phases:

 

1. Shuffle

The Reducer copies the sorted output from each Mapper using HTTP across the network.

 

2. Sort

The framework merge sorts Reducer inputs by keys (since different Mappers may have output the same key).

 

The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they are merged.

 

SecondarySort

To achieve a secondary sort on the values returned by the value iterator, the application should extend the key with the secondary key and define a grouping comparator. The keys will be sorted using the entire key, but will be grouped using the grouping comparator to decide which keys and values are sent in the same call to reduce.

 

3. Reduce

In this phase the reduce(Object, Iterable, Context) method is called for each <key, (collection of values)> in the sorted inputs.

 

The output of the reduce task is typically written to a RecordWriter via TaskInputOutputContext.write(Object, Object).

 

The output of the Reducer is not re-sorted.

 

Reference: org.apache.hadoop.mapreduce, Class

Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>

 

 

QUESTION 26

 

MapReduce is well-suited for all of the following applications EXCEPT? (Choose one):

 

A.

Text mining on a large collections of unstructured documents.

B.

Analysis of large amounts of Web logs (queries, clicks, etc.).

C.

Online transaction processing (OLTP) for an e-commerce Website.

D.

Graph mining on a large social network (e.g., Facebook friends network).

 

Answer: C

Explanation: Hadoop Map/Reduce is designed for batch-oriented work load. MapReduce is well suited for data warehousing (OLAP), but not for OLTP.

 

 

QUESTION 27

Your client application submits a MapReduce job to your Hadoop cluster. The Hadoop framework looks for an available slot to schedule the MapReduce operations on which of the following Hadoop computing daemons?

 

A.

DataNode

B.

NameNode

C.

JobTracker

D.

TaskTracker

E.

Secondary NameNode

 

Answer: C

Explanation: JobTracker is the daemon service for submitting and tracking MapReduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. Each slave node is configured with job tracker node location. The JobTracker is single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted. JobTracker in Hadoop performs following actions(from Hadoop Wiki:)

 

Client applications submit jobs to the Job tracker. The JobTracker talks to the NameNode to determine the location of the data The JobTracker locates TaskTracker nodes with available slots at or near the data The JobTracker submits the work to the chosen TaskTracker nodes. The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker.

 

 

 

 

 

A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable. When the work is completed, the JobTracker updates its status.

 

Client applications can poll the JobTracker for information.

 

Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, What is a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster?

 

 

QUESTION 28

Which statement best describes the data path of intermediate key-value pairs (i.e., output of the mappers)?

 

A.

Intermediate key-value pairs are written to HDFS. Reducers read the intermediate data from HDFS.

B.

Intermediate key-value pairs are written to HDFS. Reducers copy the intermediate data to the local disks of the machines running the reduce tasks.

C.

Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and then copied to the machine running the reduce tasks.

D.

Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and are then copied to HDFS. Reducers read the intermediate data from HDFS.

 

Answer: C

Explanation: The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.

 

Note:

* Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer. Though the reducer progress is updated still the programmer defined reduce method is called only after all the mappers have finished.

 

 

 

 

 

* Reducer is input the grouped output of a Mapper. In the phase the framework, for each Reducer, fetches the relevant partition of the output of all the Mappers, via HTTP.

 

* Mapper maps input key/value pairs to a set of intermediate key/value pairs.

 

Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs.

 

* All intermediate values associated with a given output key are subsequently grouped by the framework, and passed to the Reducer(s) to determine the final output.

 

Reference: Questions & Answers for Hadoop MapReduce developers, Where is the Mapper Output (intermediate kay-value data) stored ?

 

 

QUESTION 29

Identify the tool best suited to import a portion of a relational database every day as files into HDFS, and generate Java classes to interact with that imported data?

 

A.

Oozie

B.

Flume

C.

Pig

D.

Hue

E.

Hive

F.

Sqoop

G.

fuse-dfs

 

Answer: F

Explanation: Answer: C, E

Sqoop (“SQL-to-Hadoop”) is a straightforward command-line tool with the following capabilities:

 

Imports individual tables or entire databases to files in HDFS Generates Java classes to allow you to interact with your imported data Provides the ability to import from SQL databases straight into your Hive data warehouse

 

 

 

 

 

Note:

Data Movement Between Hadoop and Relational Databases Data can be moved between Hadoop and a relational database as a bulk data transfer, or relational tables can be accessed from within a MapReduce map function.

 

Note:

* Cloudera’s Distribution for Hadoop provides a bulk data transfer tool (i.e., Sqoop) that imports individual tables or entire databases into HDFS files. The tool also generates Java classes that support interaction with the imported data. Sqoop supports all relational databases over JDBC, and Quest Software provides a connector (i.e., OraOop) that has been optimized for access to data residing in Oracle databases.

 

Reference:http://log.medcl.net/item/2011/08/hadoop-and-mapreduce-big-data-analytics- gartner/(Data Movement between hadoop and relational databases, second paragraph)

 

 

QUESTION 30

Does the MapReduce programming model provide a way for reducers to communicate with each other?

 

A.

Yes, all reducers can communicate with each other by passing information through the jobconf object.

B.

Yes, reducers can communicate with each other by dispatching intermediate key value pairs that get shuffled to another reduce

C.

Yes, reducers running on the same machine can communicate with each other through shared memory, but not reducers on different machines.

D.

No, each reducer runs independently and in isolation.

 

Answer: D

Explanation: MapReduce programming model does not allow reducers to communicate with each other. Reducers run in isolation.

Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers

 

http://www.fromdev.com/2010/12/interview-questions-hadoop-mapreduce.html(See question no. 9)

 

 

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QUESTION 21

You need to move a file titled “weblogs” into HDFS. When you try to copy the file, you can’t. You know you have ample space on your DataNodes. Which action should you take to relieve this situation and store more files in HDFS?

 

A.

Increase the block size on all current files in HDFS.

 

 

 

 

B.

Increase the block size on your remaining files.

C.

Decrease the block size on your remaining files.

D.

Increase the amount of memory for the NameNode.

E.

Increase the number of disks (or size) for the NameNode.

F.

Decrease the block size on all current files in HDFS.

 

Answer: C

Explanation:

Note:

* -put localSrc destCopies the file or directory from the local file system identified by localSrc to dest within the DFS.

* What is HDFS Block size? How is it different from traditional file system block size?

 

In HDFS data is split into blocks and distributed across multiple nodes in the cluster. Each block is typically 64Mb or 128Mb in size. Each block is replicated multiple times. Default is to replicate each block three times. Replicas are stored on different nodes. HDFS utilizes the local file system to store each HDFS block as a separate file. HDFS Block size can not be compared with the traditional file system block size.

 

 

QUESTION 22

If you run the word count MapReduce program with m mappers and r reducers, how many output files will you get at the end of the job? And how many key-value pairs will there be in each file? Assume k is the number of unique words in the input files.

 

A.

There will be r files, each with exactly k/r key-value pairs.

B.

There will be r files, each with approximately k/m key-value pairs.

C.

There will be r files, each with approximately k/r key-value pairs.

D.

There will be m files, each with exactly k/m key value pairs.

E.

There will be m files, each with approximately k/m key-value pairs.

 

Answer: A

Explanation:Note:

* A MapReduce job with m mappers and r reducers involves up to m * r distinct copy operations, since each mapper may have intermediate output going to every reducer.

* In the canonical example of word counting, a key-value pair is emitted for every word found. For example, if we had 1,000 words, then 1,000 key-value pairs will be emitted from

 

 

 

 

 

the mappers to the reducer(s).

 

 

QUESTION 23

You are running a job that will process a single InputSplit on a cluster which has no other jobs currently running. Each node has an equal number of open Map slots. On which node will Hadoop first attempt to run the Map task?

 

A.

The node with the most memory

B.

The node with the lowest system load

C.

The node on which this InputSplit is stored

D.

The node with the most free local disk space

 

Answer: C

Explanation: The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster workcan be delegated. When the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.

 

 

QUESTION 24

When is the earliest point at which the reduce method of a given Reducer can be called?

 

A.

As soon as at least one mapper has finished processing its input split.

B.

As soon as a mapper has emitted at least one record.

C.

Not until all mappers have finished processing all records.

D.

It depends on the InputFormat used for the job.

 

Answer: C

Explanation: In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from

 

 

 

 

 

the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished.

 

Note: The reduce phase has 3 steps: shuffle, sort, reduce. Shuffle is where the data is collected by the reducer from each mapper. This can happen while mappers are generating data since it is only a data transfer. On the other hand, sort and reduce can only start once all the mappers are done.

 

Why is starting the reducers early a good thing? Because it spreads out the data transfer from the mappers to the reducers over time, which is a good thing if your network is the bottleneck.

 

Why is starting the reducers early a bad thing? Because they “hog up” reduce slots while only copying data. Another job that starts later that will actually use the reduce slots now can’t use them.

 

You can customize when the reducers startup by changing the default value of mapred.reduce.slowstart.completed.maps in mapred-site.xml. A value of 1.00 will wait for all the mappers to finish before starting the reducers. A value of 0.0 will start the reducers right away. A value of 0.5 will start the reducers when half of the mappers are complete. You can also change mapred.reduce.slowstart.completed.maps on a job-by-job basis.

 

Typically, keep mapred.reduce.slowstart.completed.maps above 0.9 if the system ever has multiple jobs running at once. This way the job doesn’t hog up reducers when they aren’t doing anything but copying data. If you only ever have one job running at a time, doing 0.1 would probably be appropriate.

 

Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, When is the reducers are started in a MapReduce job?

 

 

QUESTION 25

Assuming default settings, which best describes the order of data provided to a reducer’s reduce method:

 

A.

The keys given to a reducer aren’t in a predictable order, but the values associated with those keys always are.

 

 

 

 

B.

Both the keys and values passed to a reducer always appear in sorted order.

C.

Neither keys nor values are in any predictable order.

D.

The keys given to a reducer are in sorted order but the values associated with each key are in no predictable order

 

Answer: D

Explanation: Reducer has 3 primary phases:

 

1. Shuffle

The Reducer copies the sorted output from each Mapper using HTTP across the network.

 

2. Sort

The framework merge sorts Reducer inputs by keys (since different Mappers may have output the same key).

 

The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they are merged.

 

SecondarySort

To achieve a secondary sort on the values returned by the value iterator, the application should extend the key with the secondary key and define a grouping comparator. The keys will be sorted using the entire key, but will be grouped using the grouping comparator to decide which keys and values are sent in the same call to reduce.

 

3. Reduce

In this phase the reduce(Object, Iterable, Context) method is called for each <key, (collection of values)> in the sorted inputs.

 

The output of the reduce task is typically written to a RecordWriter via TaskInputOutputContext.write(Object, Object).

 

The output of the Reducer is not re-sorted.

 

Reference: org.apache.hadoop.mapreduce, Class

Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>

 

 

QUESTION 26

 

MapReduce is well-suited for all of the following applications EXCEPT? (Choose one):

 

A.

Text mining on a large collections of unstructured documents.

B.

Analysis of large amounts of Web logs (queries, clicks, etc.).

C.

Online transaction processing (OLTP) for an e-commerce Website.

D.

Graph mining on a large social network (e.g., Facebook friends network).

 

Answer: C

Explanation: Hadoop Map/Reduce is designed for batch-oriented work load. MapReduce is well suited for data warehousing (OLAP), but not for OLTP.

 

 

QUESTION 27

Your client application submits a MapReduce job to your Hadoop cluster. The Hadoop framework looks for an available slot to schedule the MapReduce operations on which of the following Hadoop computing daemons?

 

A.

DataNode

B.

NameNode

C.

JobTracker

D.

TaskTracker

E.

Secondary NameNode

 

Answer: C

Explanation: JobTracker is the daemon service for submitting and tracking MapReduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. Each slave node is configured with job tracker node location. The JobTracker is single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted. JobTracker in Hadoop performs following actions(from Hadoop Wiki:)

 

Client applications submit jobs to the Job tracker. The JobTracker talks to the NameNode to determine the location of the data The JobTracker locates TaskTracker nodes with available slots at or near the data The JobTracker submits the work to the chosen TaskTracker nodes. The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker.

 

 

 

 

 

A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable. When the work is completed, the JobTracker updates its status.

 

Client applications can poll the JobTracker for information.

 

Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, What is a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster?

 

 

QUESTION 28

Which statement best describes the data path of intermediate key-value pairs (i.e., output of the mappers)?

 

A.

Intermediate key-value pairs are written to HDFS. Reducers read the intermediate data from HDFS.

B.

Intermediate key-value pairs are written to HDFS. Reducers copy the intermediate data to the local disks of the machines running the reduce tasks.

C.

Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and then copied to the machine running the reduce tasks.

D.

Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and are then copied to HDFS. Reducers read the intermediate data from HDFS.

 

Answer: C

Explanation: The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.

 

Note:

* Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer. Though the reducer progress is updated still the programmer defined reduce method is called only after all the mappers have finished.

 

 

 

 

 

* Reducer is input the grouped output of a Mapper. In the phase the framework, for each Reducer, fetches the relevant partition of the output of all the Mappers, via HTTP.

 

* Mapper maps input key/value pairs to a set of intermediate key/value pairs.

 

Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs.

 

* All intermediate values associated with a given output key are subsequently grouped by the framework, and passed to the Reducer(s) to determine the final output.

 

Reference: Questions & Answers for Hadoop MapReduce developers, Where is the Mapper Output (intermediate kay-value data) stored ?

 

 

QUESTION 29

Identify the tool best suited to import a portion of a relational database every day as files into HDFS, and generate Java classes to interact with that imported data?

 

A.

Oozie

B.

Flume

C.

Pig

D.

Hue

E.

Hive

F.

Sqoop

G.

fuse-dfs

 

Answer: F

Explanation: Answer: C, E

Sqoop (“SQL-to-Hadoop”) is a straightforward command-line tool with the following capabilities:

 

Imports individual tables or entire databases to files in HDFS Generates Java classes to allow you to interact with your imported data Provides the ability to import from SQL databases straight into your Hive data warehouse

 

 

 

 

 

Note:

Data Movement Between Hadoop and Relational Databases Data can be moved between Hadoop and a relational database as a bulk data transfer, or relational tables can be accessed from within a MapReduce map function.

 

Note:

* Cloudera’s Distribution for Hadoop provides a bulk data transfer tool (i.e., Sqoop) that imports individual tables or entire databases into HDFS files. The tool also generates Java classes that support interaction with the imported data. Sqoop supports all relational databases over JDBC, and Quest Software provides a connector (i.e., OraOop) that has been optimized for access to data residing in Oracle databases.

 

Reference:http://log.medcl.net/item/2011/08/hadoop-and-mapreduce-big-data-analytics- gartner/(Data Movement between hadoop and relational databases, second paragraph)

 

 

QUESTION 30

Does the MapReduce programming model provide a way for reducers to communicate with each other?

 

A.

Yes, all reducers can communicate with each other by passing information through the jobconf object.

B.

Yes, reducers can communicate with each other by dispatching intermediate key value pairs that get shuffled to another reduce

C.

Yes, reducers running on the same machine can communicate with each other through shared memory, but not reducers on different machines.

D.

No, each reducer runs independently and in isolation.

 

Answer: D

Explanation: MapReduce programming model does not allow reducers to communicate with each other. Reducers run in isolation.

Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers

 

http://www.fromdev.com/2010/12/interview-questions-hadoop-mapreduce.html(See question no. 9)

 

 

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