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CCD-410 Exam Prep Total Q&A: 60 Questions and Answers
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NO.1 You write MapReduce job to process 100 files in HDFS. Your MapReduce
algorithm uses
TextInputFormat: the mapper applies a regular expression over
input values and emits key-values
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 reduces to one and
settings the
number of reducers to zero.
A. There is no difference in
output 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 of matching patterns are stored 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
are gathered together in one file on HDFS.
Answer: D
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Explanation:
* It is legal to set the
number of reduce-tasks to zero if no reduction is desired.
In this case the
outputs of the map-tasks go directly to the FileSystem, into the output path set
by
setOutputPath(Path). The framework does not sort the map-outputs before
writing them out to the
FileSystem.
* Often, you may want to process input
data using a map function only. To do this, simply set
mapreduce.job.reduces
to zero. The MapReduce framework will not create any reducer tasks.
Rather,
the outputs of the mapper tasks will be the final output of the
job.
Note:
Reduce
In this phase the reduce(WritableComparable,
Iterator, OutputCollector, Reporter) method is
called for each <key, (list
of values)> pair in the grouped inputs.
The output of the reduce task is
typically written to the FileSystem
via
OutputCollector.collect(WritableComparable, Writable).
Applications
can use the Reporter to report progress, set application-level status messages
and
update Counters, or just indicate that they are alive.
The output of
the Reducer is not sorted.
NO.2 For each intermediate key, each reducer
task can emit:
A. As many final key-value pairs as desired. There are no
restrictions on the types of those key-value
pairs (i.e., they can be
heterogeneous).
B. As many final key-value pairs as desired, but they must
have the same type as the intermediate
key-value pairs.
C. As many final
key-value pairs as desired, as long as all the keys have the same type and all
the
values have the same type.
D. One final key-value pair per value
associated with the key; no restrictions on the type.
E. One final key-value
pair per key; no restrictions on the type.
Answer: C
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Reference: Hadoop Map-Reduce Tutorial; Yahoo! Hadoop Tutorial,
Module 4: MapReduce
NO.3 You've written a MapReduce job that will process
500 million input records and generated 500
million key-value pairs. The data
is not uniformly distributed. Your MapReduce job will create a
significant
amount of intermediate data that it needs to transfer between mappers and
reduces
which is a potential bottleneck. A custom implementation of which
interface is most likely to reduce
the amount of intermediate data
transferred across the network?
A. Partitioner
B. OutputFormat
C.
WritableComparable
D. Writable
E. InputFormat
F. Combiner
Answer:
F
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Explanation:
Combiners are used to
increase the efficiency of a MapReduce program. They are used to
aggregate
intermediate map output locally on individual mapper outputs.
Combiners can help you reduce the
amount of data that needs to be transferred
across to the reducers. You can use your reducer code
as a combiner if the
operation performed is commutative and associative.
Reference: 24 Interview
Questions & Answers for Hadoop MapReduce developers, What are
combiners?
When should I use a combiner in my MapReduce Job?
NO.4 In a MapReduce
job, the reducer receives all values associated with same key. Which
statement
best describes the ordering of these values?
A. The values are
in sorted order.
B. The values are arbitrarily ordered, and the ordering may
vary from run to run of the same
MapReduce job.
C. The values are
arbitrary ordered, but multiple runs of the same MapReduce job will always
have
the same ordering.
D. Since the values come from mapper outputs, the
reducers will receive contiguous sections of
sorted values.
Answer:
B
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Explanation:
Note:
*Input to the Reducer is the sorted output
of the mappers.
*The framework calls the application's Reduce function once
for each unique key in the sorted
order.
*Example:
For the given sample
input the first map emits:
< Hello, 1>
< World, 1>
<
Bye, 1>
< World, 1>
The second map emits:
< Hello,
1>
< Hadoop, 1>
< Goodbye, 1>
< Hadoop,
1>
NO.5 You want to understand more about how users browse your public
website, such as which
pages they visit prior to placing an order. You have a
farm of 200 web servers hosting your website.
How will you gather this data
for your analysis?
A. Ingest the server web logs into HDFS using Flume.
B.
Write a MapReduce job, with the web servers for mappers, and the Hadoop cluster
nodes for
reduces.
C. Import all users' clicks from your OLTP databases
into Hadoop, using Sqoop.
D. Channel these clickstreams inot Hadoop using
Hadoop Streaming.
E. Sample the weblogs from the web servers, copying them
into Hadoop using curl.
Answer: A
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NO.6 On a cluster running MapReduce v1 (MRv1), a TaskTracker
heartbeats into the JobTracker on
your cluster, and alerts the JobTracker it
has an open map task slot.
What determines how the JobTracker assigns each
map task to a TaskTracker?
A. The amount of RAM installed on the TaskTracker
node.
B. The amount of free disk space on the TaskTracker node.
C. The
number and speed of CPU cores on the TaskTracker node.
D. The average system
load on the TaskTracker node over the past fifteen (15) minutes.
E. The
location of the InsputSplit to be processed in relation to the location of the
node.
Answer: E
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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
work can 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.
Reference: 24
Interview Questions & Answers for Hadoop MapReduce developers, How
JobTracker
schedules a task?
NO.7 Table metadata in Hive is:
A.
Stored as metadata on the NameNode.
B. Stored along with the data in
HDFS.
C. Stored in the Metastore.
D. Stored in ZooKeeper.
Answer:
C
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Explanation:
By default, hive use an embedded Derby database to store
metadata information.
The metastore is the "glue" between Hive and HDFS. It
tells Hive where your data files live in
HDFS, what type of data they
contain, what tables they belong to, etc.
The Metastore is an application
that runs on an RDBMS and uses an open source ORM layer
called DataNucleus,
to convert object representations into a relational schema and vice
versa.
They chose this approach as opposed to storing this information in
hdfs as they need the
Metastore to be very low latency. The DataNucleus layer
allows them to plugin many different
RDBMS technologies.
Note:
*By
default, Hive stores metadata in an embedded Apache Derby database, and
other
client/server databases like MySQL can optionally be used.
*features
of Hive include:
Metadata storage in an RDBMS, significantly reducing the
time to perform semantic checks during
query execution.
Reference: Store
Hive Metadata into RDBMS
NO.8 To process input key-value pairs, your
mapper needs to lead a 512 MB data file in memory.
What is the best way to
accomplish this?
A. Serialize the data file, insert in it the JobConf object,
and read the data into memory in the
configure method of the mapper.
B.
Place the data file in the DistributedCache and read the data into memory in the
map method of
the mapper.
C. Place the data file in the DataCache and read
the data into memory in the configure method of the
mapper.
D. Place the
data file in the DistributedCache and read the data into memory in the configure
method
of the mapper.
Answer: C
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