Hi,

   I came across the following question in some sites and the answer that
they provided seems to be wrong according to me... I might be wrong... Can
some one help on confirming the right answers for these 11 questions pls..
appreciate the explanation if you could able to provide...

*******************************************************************************
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

My Answer            : C
Answer Given in site : A

*******************************************************************************
What is a Writable?
A. Writable is an interface that all keys and values in MapReduce must
implement. Classes implementing this interface must implement methods
forserializingand deserializing themselves.
B. Writable is an abstract class that all keys and values in MapReduce must
extend. Classes extending this abstract base class must implementmethods
for serializing and deserializingthemselves
C. Writable is an interface that all keys, but not values, in MapReduce
must implement. Classes implementing this interface mustimplementmethods
for serializing and deserializing themselves.
D. Writable is an abstract class that all keys, but not values, in
MapReduce must extend. Classes extending this abstract base class must
implementmethods for serializing and deserializing themselves.

My Answer            : A
Answer Given in site : B

*******************************************************************************

You write a MapReduce job to process 100 files in HDFS. Your MapReducc
algorithm uses
TextInputFormat and the IdentityReducer: the mapper applies a regular
expression over input
values and emits key-value pairs with the key consisting of the matching
text, and the value
containing the filename and byte offset. Determine the difference between
setting the number of
reducers to zero.
A. There is no differenceinoutput between the two settings.
B. With zero reducers, no reducer runs and the job throws an exception.
With one reducer,
instances of matching patterns are stored in a single file on HDFS.
C. With zero reducers, all instances of matching patterns are gathered
together in one file on
HDFS. With one reducer, instances ofmatching patternsstored in multiple
files on HDFS.
D. With zero reducers, instances of matching patterns are stored in
multiple files on HDFS. With
one reducer, all instances of matching patterns aregathered together in one
file on HDFS.

My Answer            : D
Answer Given in site : C

*******************************************************************************

During the standard sort and shuffle phase of MapReduce, keys and values
are passed to
reducers. Which of the following is true?
A. Keys are presented to a reducerin sorted order; values foragiven key are
not sorted.
B. Keys are presented to a reducer in soiled order; values for a given key
are sorted in ascending
order.
C. Keys are presented to a reducer in random order; values for a given key
are not sorted.
D. Keys are presented to a reducer in random order; values for a given key
are sorted in
ascending order.

My Answer            : A
Answer Given in site : D

*******************************************************************************

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 runningthe 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 machinerunning thereduce 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 theintermediate data from
HDFS.

My Answer            : C
Answer Given in site : B

*******************************************************************************

You are developing a combiner that takes as input Text keys, IntWritable
values, and emits Text
keys, Intwritable values. Which interface should your class implement?
A. Mapper <Text, IntWritable, Text, IntWritable>
B. Reducer <Text, Text, IntWritable, IntWritable>
C. Reducer <Text, IntWritable, Text, IntWritable>
D. Combiner <Text, IntWritable, Text, IntWritable>
E. Combiner <Text, Text, IntWritable, IntWritable>

My Answer            : D
Answer Given in site : C

*******************************************************************************

What happens in a MapReduce job when you set the number of reducers to one?
A. A single reducer gathers and processes all the output from all the
mappers. The output is
written in as many separate files as there are mappers.
B. A single reducer gathers and processes all the output from all the
mappers. The output is
written to a single file in HDFS.
C. Setting the number of reducers to one creates a processing bottleneck,
and since the number
of reducers as specified by the programmer is used as areference value
only, the MapReduce
runtime provides a default setting for the number of reducers.
D. Setting the number of reducers to one is invalid, and an exception is
thrown

My Answer            : B
Answer Given in site : C

*******************************************************************************

In the standard word count MapReduce algorithm, why might using a combiner
reduce the overall
Job running time?
A. Because combiners perform local aggregation of word counts, thereby
allowing the mappers to
process input data faster.
B

Ok...
Where are you pulling these questions from?

Seriously.
On Nov 7, 2012, at 11:21 AM, Ramasubramanian Narayanan <[EMAIL PROTECTED]> wrote:

> Hi,
>
>    I came across the following question in some sites and the answer that they provided seems to be wrong according to me... I might be wrong... Can some one help on confirming the right answers for these 11 questions pls.. appreciate the explanation if you could able to provide...
>
> *******************************************************************************
> 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
>
> My Answer            : C
> Answer Given in site : A
>
> *******************************************************************************
> What is a Writable?
> A. Writable is an interface that all keys and values in MapReduce must implement. Classes implementing this interface must implement methods forserializingand deserializing themselves.
> B. Writable is an abstract class that all keys and values in MapReduce must extend. Classes extending this abstract base class must implementmethods for serializing and deserializingthemselves
> C. Writable is an interface that all keys, but not values, in MapReduce must implement. Classes implementing this interface mustimplementmethods for serializing and deserializing themselves.
> D. Writable is an abstract class that all keys, but not values, in MapReduce must extend. Classes extending this abstract base class must implementmethods for serializing and deserializing themselves.
>
> My Answer            : A
> Answer Given in site : B
>
> *******************************************************************************
>
> You write a MapReduce job to process 100 files in HDFS. Your MapReducc algorithm uses
> TextInputFormat and the IdentityReducer: the mapper applies a regular expression over input
> values and emits key-value pairs with the key consisting of the matching text, and the value
> containing the filename and byte offset. Determine the difference between setting the number of
> reducers to zero.
> A. There is no differenceinoutput between the two settings.
> B. With zero reducers, no reducer runs and the job throws an exception. With one reducer,
> instances of matching patterns are stored in a single file on HDFS.
> C. With zero reducers, all instances of matching patterns are gathered together in one file on
> HDFS. With one reducer, instances ofmatching patternsstored in multiple files on HDFS.
> D. With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With
> one reducer, all instances of matching patterns aregathered together in one file on HDFS.
>
> My Answer            : D
> Answer Given in site : C
>
> *******************************************************************************
>
> During the standard sort and shuffle phase of MapReduce, keys and values are passed to
> reducers. Which of the following is true?
> A. Keys are presented to a reducerin sorted order; values foragiven key are not sorted.
> B. Keys are presented to a reducer in soiled order; values for a given key are sorted in ascending
> order.
> C. Keys are presented to a reducer in random order; values for a given key are not sorted.
> D. Keys are presented to a reducer in random order; values for a given key are sorted in
> ascending order.
>
> My Answer            : A
> Answer Given in site : D
>
> *******************************************************************************
>
> 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

nothing as consolidated...... I am collecting for the past 1 month... few
as printout and few from mails and few from googling and few from sites and
few from some of my friends...

regards,
Rams

On Wed, Nov 7, 2012 at 10:57 PM, Michael Segel <[EMAIL PROTECTED]>wrote:

> Ok...
> Where are you pulling these questions from?
>
> Seriously.
>
>
> On Nov 7, 2012, at 11:21 AM, Ramasubramanian Narayanan <
> [EMAIL PROTECTED]> wrote:
>
> > Hi,
> >
> >    I came across the following question in some sites and the answer
> that they provided seems to be wrong according to me... I might be wrong...
> Can some one help on confirming the right answers for these 11 questions
> pls.. appreciate the explanation if you could able to provide...
> >
> >
> *******************************************************************************
> > 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
> >
> > My Answer            : C
> > Answer Given in site : A
> >
> >
> *******************************************************************************
> > What is a Writable?
> > A. Writable is an interface that all keys and values in MapReduce must
> implement. Classes implementing this interface must implement methods
> forserializingand deserializing themselves.
> > B. Writable is an abstract class that all keys and values in MapReduce
> must extend. Classes extending this abstract base class must
> implementmethods for serializing and deserializingthemselves
> > C. Writable is an interface that all keys, but not values, in MapReduce
> must implement. Classes implementing this interface mustimplementmethods
> for serializing and deserializing themselves.
> > D. Writable is an abstract class that all keys, but not values, in
> MapReduce must extend. Classes extending this abstract base class must
> implementmethods for serializing and deserializing themselves.
> >
> > My Answer            : A
> > Answer Given in site : B
> >
> >
> *******************************************************************************
> >
> > You write a MapReduce job to process 100 files in HDFS. Your MapReducc
> algorithm uses
> > TextInputFormat and the IdentityReducer: the mapper applies a regular
> expression over input
> > values and emits key-value pairs with the key consisting of the matching
> text, and the value
> > containing the filename and byte offset. Determine the difference
> between setting the number of
> > reducers to zero.
> > A. There is no differenceinoutput between the two settings.
> > B. With zero reducers, no reducer runs and the job throws an exception.
> With one reducer,
> > instances of matching patterns are stored in a single file on HDFS.
> > C. With zero reducers, all instances of matching patterns are gathered
> together in one file on
> > HDFS. With one reducer, instances ofmatching patternsstored in multiple
> files on HDFS.
> > D. With zero reducers, instances of matching patterns are stored in
> multiple files on HDFS. With
> > one reducer, all instances of matching patterns aregathered together in
> one file on HDFS.
> >
> > My Answer            : D
> > Answer Given in site : C
> >
> >
> *******************************************************************************
> >
> > During the standard sort and shuffle phase of MapReduce, keys and values
> are passed to
> > reducers. Which of the following is true?
> > A. Keys are presented to a reducerin sorted order; values foragiven key
> are not sorted.
> > B. Keys are presented to a reducer in soiled order; values for a given
> key are sorted in ascending
> > order.
> > C. Keys are presented to a reducer in random order; values for a given

Sorry, I think I had better explain why I am curious...

First, there are a couple of sites that have study questions to help pass Cloudera's certification.
( I don't know if Hortonworks has cert tests, but both MapR and Cloudera do.)

Its just looking first at the questions... not really good questions and selection of answers.  Then the 'correct' answer.

I can understand if you don't want to reveal your sources publicly, but you have to understand that misinformation found in these sites makes it harder to teach the right answers.

As Harsh says, you should be able to look at the questions and then go back to Tom White's book and others to verify why you think your answer is right.

HTH

-Mike

On Nov 7, 2012, at 11:30 AM, Ramasubramanian Narayanan <[EMAIL PROTECTED]> wrote:

> nothing as consolidated...... I am collecting for the past 1 month... few as printout and few from mails and few from googling and few from sites and few from some of my friends...
>
> regards,
> Rams
>
> On Wed, Nov 7, 2012 at 10:57 PM, Michael Segel <[EMAIL PROTECTED]> wrote:
> Ok...
> Where are you pulling these questions from?
>
> Seriously.
>
>
> On Nov 7, 2012, at 11:21 AM, Ramasubramanian Narayanan <[EMAIL PROTECTED]> wrote:
>
> > Hi,
> >
> >    I came across the following question in some sites and the answer that they provided seems to be wrong according to me... I might be wrong... Can some one help on confirming the right answers for these 11 questions pls.. appreciate the explanation if you could able to provide...
> >
> > *******************************************************************************
> > 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
> >
> > My Answer            : C
> > Answer Given in site : A
> >
> > *******************************************************************************
> > What is a Writable?
> > A. Writable is an interface that all keys and values in MapReduce must implement. Classes implementing this interface must implement methods forserializingand deserializing themselves.
> > B. Writable is an abstract class that all keys and values in MapReduce must extend. Classes extending this abstract base class must implementmethods for serializing and deserializingthemselves
> > C. Writable is an interface that all keys, but not values, in MapReduce must implement. Classes implementing this interface mustimplementmethods for serializing and deserializing themselves.
> > D. Writable is an abstract class that all keys, but not values, in MapReduce must extend. Classes extending this abstract base class must implementmethods for serializing and deserializing themselves.
> >
> > My Answer            : A
> > Answer Given in site : B
> >
> > *******************************************************************************
> >
> > You write a MapReduce job to process 100 files in HDFS. Your MapReducc algorithm uses
> > TextInputFormat and the IdentityReducer: the mapper applies a regular expression over input
> > values and emits key-value pairs with the key consisting of the matching text, and the value
> > containing the filename and byte offset. Determine the difference between setting the number of
> > reducers to zero.
> > A. There is no differenceinoutput between the two settings.
> > B. With zero reducers, no reducer runs and the job throws an exception. With one reducer,
> > instances of matching patterns are stored in a single file on HDFS.
> > C. With zero reducers, all instances of matching patterns are gathered together in one file on
> > HDFS. With one reducer, instances ofmatching patternsstored in multiple files on HDFS.

Hi,

Have given my explanation for choosing and why I am saying given answer is
wrong...

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

My Answer            : C [Mapper will run on the data nodes where it has
the data. So it will run the map task on the node in which the InputSplit
is stored.]
Answer Given in site : A [I Hope the Map task will go and check the nodes
which has the most memory]
*******************************************************************************
What is a Writable?
A. Writable is an interface that all keys and values in MapReduce must
implement. Classes implementing this interface must implement methods
forserializingand deserializing themselves.
B. Writable is an abstract class that all keys and values in MapReduce must
extend. Classes extending this abstract base class must implementmethods
for serializing and deserializingthemselves
C. Writable is an interface that all keys, but not values, in MapReduce
must implement. Classes implementing this interface mustimplementmethods
for serializing and deserializing themselves.
D. Writable is an abstract class that all keys, but not values, in
MapReduce must extend. Classes extending this abstract base class must
implementmethods for serializing and deserializing themselves.

My Answer            : A [Writable is an interface]
Answer Given in site : B [Writable is not abstract class]
******************************************************************************

You write a MapReduce job to process 100 files in HDFS. Your MapReducc
algorithm uses
TextInputFormat and the IdentityReducer: the mapper applies a regular
expression over input
values and emits key-value pairs with the key consisting of the matching
text, and the value
containing the filename and byte offset. Determine the difference between
setting the number of
reducers to zero.
A. There is no differenceinoutput between the two settings.
B. With zero reducers, no reducer runs and the job throws an exception.
With one reducer,
instances of matching patterns are stored in a single file on HDFS.
C. With zero reducers, all instances of matching patterns are gathered
together in one file on
HDFS. With one reducer, instances ofmatching patternsstored in multiple
files on HDFS.
D. With zero reducers, instances of matching patterns are stored in
multiple files on HDFS. With
one reducer, all instances of matching patterns aregathered together in one
file on HDFS.

My Answer            : D [With No reducers all the output of Mappers will
be directly written to HDFS. So mutiple files will be created]
Answer Given in site : C [If you have one reducer then you will get one
output file only not many]

*******************************************************************************

During the standard sort and shuffle phase of MapReduce, keys and values
are passed to
reducers. Which of the following is true?
A. Keys are presented to a reducerin sorted order; values foragiven key are
not sorted.
B. Keys are presented to a reducer in soiled order; values for a given key
are sorted in ascending
order.
C. Keys are presented to a reducer in random order; values for a given key
are not sorted.
D. Keys are presented to a reducer in random order; values for a given key
are sorted in
ascending order.

My Answer            : A [For Reducer, Keys will be passed on Sorted order
not Value. To get the value in sorted order we need to use secondary sort]
Answer Given in site : D [For Reducer, Keys will be passsed only in sorted
order not in random order]

*******************************************************************************

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 runningthe 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 machinerunning thereduce 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 theintermediate data from
HDFS.

My Answer            : C [Intermediate key-value pairs are written in local
disk and transferred to network for reducer. Once the job is completed the
intermediate data will be deleted on the data node]
Answer Given in site : B [Intermediate key-values will not be written to
HDFS and reducer will not read from HDFS]

*******************************************************************************

You are developing a combiner that takes as input Text keys, IntWritable
values, and emits Text
keys, Intwritable values. Which interface should your class implement?
A. Mapper <Text, IntWritable, Text, IntWritable>
B. Reducer <Text, Text, IntWritable, IntWritable>
C. Reducer <Text, IntWritable, Text, IntWritable>
D. Combiner <Text, IntWritable, Text, IntWritable>
E. Combiner <Text, Text, IntWritable, IntWritable>

My Answer            : D [For developing combiner we need to use the
combiner method]
Answer Given in site : C [For developing combiner we need to use the
combiner method only not Reducer method]

*******************************************************************************

What happens in a MapReduce job when you set the number of reducers to one?
A. A single reducer gathers and processes all the output from all the
mappers. The output is
written in as many separate files as there are mappers.
B. A single reducer gathers and pr