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CCD-410 Exam

Cloudera Certified Developer for Apache Hadoop (CCDH)

  • Exam Number/Code : CCD-410
  • Exam Name : Cloudera Certified Developer for Apache Hadoop (CCDH)
  • Questions and Answers : 60 Q&As
  • Update Time: 2017-09-14
  • Price: $ 99.00 $ 39.00

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Exam Description or Exam Demo

QUESTION NO: 1
When is the earliest point at which the reduce method of a given Reducer can be called?
A. As soon as at least one mapper has finished processing its input split.
B. As soon as a mapper has emitted at least one record.
C. Not until all mappers have finished processing all records.
D. It depends on the InputFormat used for the job.
Answer: C
Explanation: In a MapReduce job reducers do not start executing the reduce method until the all
Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers
as soon as they are available. The programmer defined reduce method is called only after all the
mappers have finished.
Note:The reduce phase has 3 steps: shuffle, sort, reduce. Shuffle is where the data is collected by
the reducer from each mapper. This can happen while mappers are generating data since it is only
a data transfer. On the other hand, sort and reduce can only start once all the mappers are done.
Why is starting the reducers early a good thing? Because it spreads out the data transfer from the
mappers to the reducers over time, which is a good thing if your network is the bottleneck.
Why is starting the reducers early a bad thing? Because they "hog up" reduce slots while only
copying data. Another job that starts later that will actually use the reduce slots now can't use
them.
You can customize when the reducers startup by changing the default value of
mapred.reduce.slowstart.completed.maps in mapred-site.xml. A value of 1.00 will wait for all the
mappers to finish before starting the reducers. A value of 0.0 will start the reducers right away. A
value of 0.5 will start the reducers when half of the mappers are complete. You can also change
mapred.reduce.slowstart.completed.maps on a job-by-job basis.
Typically, keep mapred.reduce.slowstart.completed.maps above 0.9 if the system ever has
multiple jobs running at once. This way the job doesn't hog up reducers when they aren't doing
anything but copying data. If you only ever have one job running at a time, doing 0.1 would
probably be appropriate.
Reference:24 Interview Questions & Answers for Hadoop MapReduce developers,When is the
reducers are started in a MapReduce job?
QUESTION NO: 2
Which describes how a client reads a file from HDFS?
A. The client queries the NameNode for the block location(s). The NameNode returns the block
location(s) to the client. The client reads the data directory off the DataNode(s).
B. The client queries all DataNodes in parallel. The DataNode that contains the requested data
responds directly to the client. The client reads the data directly off the DataNode.
C. The client contacts the NameNode for the block location(s). The NameNode then queries the
DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode
redirects the client to the DataNode that holds the requested data block(s). The client then reads
the data directly off the DataNode.
D. The client contacts the NameNode for the block location(s). The NameNode contacts the
DataNode that holds the requested data block. Data is transferred from the DataNode to the
NameNode, and then from the NameNode to the client.
Answer: C
Explanation: The Client communication to HDFS happens using Hadoop HDFS API. Client
applications talk to the NameNode whenever they wish to locate a file, or when they want to
add/copy/move/delete a file on HDFS. The NameNode responds the successful requests by
returning a list of relevant DataNode servers where the data lives. Client applications can talk
directly to a DataNode, once the NameNode has provided the location of the data.
Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers,How the Client
communicates with HDFS?
QUESTION NO: 3
You are developing a combiner that takes as input Text keys, IntWritable values, and emits Text
keys, IntWritable values. Which interface should your class implement?
A. Combiner <Text, IntWritable, Text, IntWritable>
B. Mapper <Text, IntWritable, Text, IntWritable>
C. Reducer <Text, Text, IntWritable, IntWritable>
D. Reducer <Text, IntWritable, Text, IntWritable>
E. Combiner <Text, Text, IntWritable, IntWritable>
Answer: D
Explanation:
QUESTION NO: 4
Indentify the utility that allows you to create and run MapReduce jobs with any executable or script
as the mapper and/or the reducer?
A. Oozie
B. Sqoop
C. Flume
D. Hadoop Streaming
E. mapred
Answer: D
Explanation: Hadoop streaming is a utility that comes with the Hadoop distribution. The utility
allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or
the reducer.
Reference:http://hadoop.apache.org/common/docs/r0.20.1/streaming.html(Hadoop Streaming,
second sentence)
QUESTION NO: 5
How are keys and values presented and passed to the reducers during a standard sort and shuffle
phase of MapReduce?
A. Keys are presented to reducer in sorted order; values for a given key are not sorted.
B. Keys are presented to reducer in sorted order; values for a given key are sorted in ascending
order.
C. Keys are presented to a reducer in random order; values for a given key are not sorted.
D. Keys are presented to a reducer in random order; values for a given key are sorted in
ascending order.
Answer: A
Explanation: Reducer has 3 primary phases:
1.Shuffle
The Reducer copies the sorted output from each Mapper using HTTP across the network.
2.Sort
The framework merge sorts Reducer inputs by keys (since different Mappers may have output the
same key).
The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they are
merged.
SecondarySort
To achieve a secondary sort on the values returned by the value iterator, the application should
extend the key with the secondary key and define a grouping comparator. The keys will be sorted
using the entire key, but will be grouped using the grouping comparator to decide which keys and
values are sent in the same call to reduce.
3.Reduce
In this phase the reduce(Object, Iterable, Context) method is called for each <key, (collection of
values)> in the sorted inputs.
The output of the reduce task is typically written to a RecordWriter via
TaskInputOutputContext.write(Object, Object).
The output of the Reducer is not re-sorted.
Reference: org.apache.hadoop.mapreduce,Class
Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>
QUESTION NO: 6
Assuming default settings, which best describes the order of data provided to a reducer’s reduce
method:
A. The keys given to a reducer aren’t in a predictable order, but the values associated with those
keys always are.
B. Both the keys and values passed to a reducer always appear in sorted order.
C. Neither keys nor values are in any predictable order.
D. The keys given to a reducer are in sorted order but the values associated with each key are in
no predictable order
Answer: D
Explanation: Reducer has 3 primary phases:
1.Shuffle
The Reducer copies the sorted output from each Mapper using HTTP across the network.
2.Sort
The framework merge sorts Reducer inputs by keys (since different Mappers may have output the
same key).
The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they are
merged.
SecondarySort
To achieve a secondary sort on the values returned by the value iterator, the application should
extend the key with the secondary key and define a grouping comparator. The keys will be sorted
using the entire key, but will be grouped using the grouping comparator to decide which keys and
values are sent in the same call to reduce.
3.Reduce
In this phase the reduce(Object, Iterable, Context) method is called for each <key, (collection of
values)> in the sorted inputs.
The output of the reduce task is typically written to a RecordWriter via
TaskInputOutputContext.write(Object, Object).
The output of the Reducer is not re-sorted.
Reference: org.apache.hadoop.mapreduce,Class
Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>


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