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Exam Name: Cloudera Certified Developer for Apache Hadoop (CCDH)
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CCD-410 Test Questions Total Q&A: 60 Questions and Answers
Last Update: 08-03,2015
CCD-410 Real Exams Detail: CCD-410 Test Questions
Begin Your Journey to Developer Certification
This exam focuses on engineering data solutions in MapReduce and understanding the Hadoop ecosystem (including Hive, Pig, Sqoop, Oozie, Crunch, and Flume). Candidates who successfully pass CCD–410 are awarded the Cloudera Certified Hadoop Developer (CCDH) credential.
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
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Reference: Hadoop
Map-Reduce Tutorial; Yahoo! Hadoop Tutorial, Module 4: MapReduce
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 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.4 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.5 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.6 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.7 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.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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