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試験科目:「Cloudera Certified Developer for Apache Hadoop (CCDH)」
最近更新時間:2015-07-10
問題と解答:60
CCD-410 Study Guide
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.
Recommended Cloudera Training Course
Cloudera Developer Training for
Apache Hadoop
Practice Test
CCD–410 Practice Test Subscription
Exam Sections
Each candidate receives 50 - 55 live
questions. Questions are delivered dynamically and based on difficulty ratings
so that each candidate receives an exam at a consistent level. Each test also
includes at least five unscored, experimental (beta) questions.
Infrastructure: Hadoop components that are outside the concerns of a
particular MapReduce job that a developer needs to master (25%)
Data
Management: Developing, implementing, and executing commands to properly manage
the full data lifecycle of a Hadoop job (30%)
Job Mechanics: The processes
and commands for job control and execution with an emphasis on the process
rather than the data (25%)
Querying: Extracting information from data
(20%)
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NO.1 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 CCD-410問題 CCD-410
vue
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.2 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.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
Cloudera難易度 CCD-410 CCD-410コマンド CCD-410無料
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 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 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過去問題 CCD-410 CCD-410目的 CCD-410返済
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.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
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 CCD-410ソリューション CCD-410模試エンジン CCD-410認定試験
Reference:
Hadoop Map-Reduce Tutorial; Yahoo! Hadoop Tutorial, Module 4:
MapReduce
NO.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
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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.
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