NO.1 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
Cloudera Practice
Exam CCD-410 CCD-410
original questionsReference: Hadoop Map-Reduce Tutorial; Yahoo!
Hadoop Tutorial, Module 4: MapReduce
NO.2 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
Cloudera test
answers CCD-410 CCD-410 CCD-410Explanation:
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.3 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
Cloudera exam
prep CCD-410 CCD-410NO.4
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
Cloudera Braindumps CCD-410 original
questions CCD-410 exam dumps CCD-410
Actual TestExplanation:
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.5 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
Cloudera Exam Prep CCD-410 Dumps
PDF CCD-410 pdfExplanation:
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.6 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
Cloudera
certification CCD-410 Practice
Exam CCD-410 dumpsExplanation:
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.7 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
Cloudera CCD-410 VCE
Dumps CCD-410 CCD-410 Exam
Prep CCD-410 testNO.8 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
Cloudera
Braindumps CCD-410 Exam PDF CCD-410Explanation:
* 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.
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CCD-410 Actual Test Total Q&A: 60 Questions and
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