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Exam Code:
CCD-410Exam Name: Cloudera Certified Developer for Apache Hadoop (CCDH)
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CCD-410 Exam Questions Total Q&A: 60 Questions and Answers
Last Update: 09-03,2015
CCD-410 Training online Detail: CCD-410 Exam Questions
NO.1 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.2 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.3 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.4 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.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
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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.6 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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NO.7 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.8 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?
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