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From Ramasubramanian Narayanan <ramasubramanian.naraya...@gmail.com>
Subject Please help on providing correct answers
Date Wed, 07 Nov 2012 17:21:01 GMT
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. Because combiners perform local aggregation of word counts, thereby
reducing the number of
mappers that need to run.
C. Because combiners perform local aggregation of word counts, and then
transfer that data to
reducers without writing the intermediatedata to disk.
D. Because combiners perform local aggregation of word counts, thereby
reducing the number of
key-value pairs that need to be snuff letacross thenetwork to the reducers.

My Answer            : C
Answer Given in site : A

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

You need to create a GUI application to help your company's sales people
add and edit customer
information. Would HDFS be appropriate for this customer information file?
A. Yes, because HDFS isoptimized forrandom access writes.
B. Yes, because HDFS is optimized for fast retrieval of relatively small
amounts of data.
C. No, becauseHDFS can only be accessed by MapReduce applications.
D. No, because HDFS is optimized for write-once, streaming access for
relatively large files.

My Answer            : D
Answer Given in site : A

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

You need to create a job that does frequency analysis on input data. You
will do this by writing a
Mapper that uses TextInputForma and splits each value (a line of text from
an input file) into
individual characters. For each one of these characters, you will emit the
character as a key and
as IntWritable as the value. Since this will produce proportionally more
intermediate data than
input data, which resources could you expect to be likely bottlenecks?
A. Processor and RAM
B. Processor and disk I/O
C. Disk I/O and network I/O
D. Processor and network I/O

My Answer            : D
Answer Given in site : B

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

Which of the following statements best describes how a large (100 GB) file
is stored in HDFS?
A. The file is divided into variable size blocks, which are stored on
multiple data nodes. Each block
is replicated three timesby default.
B. The file is replicated three times by default. Each ropy of the file is
stored on a separate
datanodes.
C. The master copy of the file is stored on a single datanode. The replica
copies are divided into
fixed-size blocks, which are stored on multiple datanodes.
D. The file is divided into fixed-size blocks, which are stored on multiple
datanodes.Eachblock is
replicated three times by default. Multiple blocks from the same file
mightreside on the same
datanode.
E. The tile is divided into fixed-sizeblocks, which are stored on multiple
datanodes.Eachblock is
replicated three times by default.HDES guarantees that different blocks
from the same file are
never on the same datanode.

My Answer            : D
Answer Given in site : B

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

regards,
Rams

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