Big data hadoop practice test2

Thank you very much for taking test

Test your caliber

1. In a MapReduce job, you want each of your input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?
Increase the parameter that controls minimum split size in the job configuration.	Write a custom MapRunner that iterates over all key-value pairs in the entire file.
Set the number of mappers equal to the number of input files you want to process.	Write a custom FileInputFormat and override the method isSplitable to always return false.

2. Which process describes the lifecycle of a Mapper?
The JobTracker calls the TaskTracker’s configure () method, then its map () method and finally its close () method.	The TaskTracker spawns a new Mapper to process all records in a single input split.
The TaskTracker spawns a new Mapper to process each key-value pair.	The JobTracker spawns a new Mapper to process all records in a single file.

3. Determine which best describes when the reduce method is first called in a MapReduce job?
Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The programmer can configure in the job what percentage of the intermediate data should arrive before the reduce method begins.	Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The reduce method is called only after all intermediate data has been copied and sorted.
Reduce methods and map methods all start at the beginning of a job, in order to provide optimal performance for map-only or reduce-only jobs.	Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The reduce method is called as soon as the intermediate key-value pairs start to arrive.

4. You have written a Mapper which invokes the following five calls to the OutputColletor.collect method: output.collect (new Text (“Apple”), new Text (“Red”) ) ; output.collect (new Text (“Banana”), new Text (“Yellow”) ) ; output.collect (new Text (“Apple”), new Text (“Yellow”) ) ; output.collect (new Text (“Cherry”), new Text (“Red”) ) ; output.collect (new Text (“Apple”), new Text (“Green”) ) ; How many times will the Reducer’s reduce method be invoked?
6	3
1	0

5. 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?
Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure method of the mapper.	Place the data file in the DistributedCache and read the data into memory in the map method of the mapper.
Place the data file in the DataCache and read the data into memory in the configure method of the mapper.	Place the data file in the DistributedCache and read the data into memory in the configure method of the mapper.

6. In a MapReduce job, the reducer receives all values associated with same key. Which statement best describes the ordering of these values?
The values are in sorted order.	The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.
The values are arbitrary ordered, but multiple runs of the same MapReduce job will always have the same ordering.	Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values.

7. You need to create a job that does frequency analysis on input data. You will do this by writing a Mapper that uses TextInputFormat and splits each value (a line of text from an input file) into individual characters. For each one of these characters, you will emit the character as a key and an InputWritable as the value. As this will produce proportionally more intermediate data than input data, which two resources should you expect to be bottlenecks?
Processor and network I/O	Disk I/O and network I/O
Processor and RAM	Processor and disk I/O

8. You want to count the number of occurrences for each unique word in the supplied input data. You’ve decided to implement this by having your mapper tokenize each word and emit a literal value 1, and then have your reducer increment a counter for each literal 1 it receives. After successful implementing this, it occurs to you that you could optimize this by specifying a combiner. Will you be able to reuse your existing Reduces as your combiner in this case and why or why not?
Yes, because the sum operation is both associative and commutative and the input and output types to the reduce method match.	No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
No, because the Reducer and Combiner are separate interfaces.	No, because the Combiner is incompatible with a mapper which doesn’t use the same data type for both the key and value.

9. Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop daemon on which the Hadoop framework will look for an available slot schedule a MapReduce operation.
TaskTracker	NameNode
DataNode	JobTracker

10. Which project gives you a distributed, Scalable, data store that allows you random, realtime read/write access to hundreds of terabytes of data?
HBase	Hue
Pig	Hive

11. What is a SequenceFile?
A Sequence Filecontains a binary encoding of an arbitrary number of homo geneous writable objects.	A Sequence Filecontains a binary encoding of an arbitrary number of hetero geneous writable objects.
A Sequence Filecontains a binary encoding of an arbitrary number of Writable Comparable objects, in sorted order.	A Sequence Filecontains a binary encoding of an arbitrary number key-value pairs. Each key must be the same type. Each value must be same type.

12. In a MapReduce job, you want each of you input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?
Increase the parameter that controls minimum split size in the job configuration.	Write a custom MapRunner that iterates over all key-value pairs in the entire file.
Set the number of mappers equal to the number of input files you want to process.	Write a custom FileInputFormat and override the method isSplittable to always return false.

13. Which of the following best describes the workings of TextInputFormat?
Input file splits may cross line breaks. A line thatcrosses tile splits is ignored.	The input file is split exactly at the line breaks, so each Record Reader will read a series of complete lines.
Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of both splits containing the brokenline.	Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the end of the broken line.

14. Which of the following statements most accurately describes the relationship between MapReduce and Pig?
Pig provides additional capabilities that allow certain types of data manipulation not possible with MapReduce.	Pig provides no additional capabilities to MapReduce. Pig programs are executed as MapReduce jobs via the Pig interpreter.
Pig programs rely on MapReduce but are extensible, allowing developers to do specialpurpose processing not provided by MapReduce.	Pig provides the additional capability of allowing you to control the flow of multiple MapReduce jobs.

15. Will settings using Java API overwrite values in configuration files?
No. The configuration settings in the configuration file takes precedence	Yes. The configuration settings using Java API take precedence
It depends when the developer reads the configuration file. If it is read first then no.	Only global configuration settings are captured in configuration files on namenode. There are only a very few job parameters that can be set using Java API.

16. What is AVRO?
Avro is a java serialization library	Avro is a java compression library
Avro is a java library that create splittable files	None of these answers are correct

17. Can you run Map - Reduce jobs directly on Avro data?
Yes, Avro was specifically designed for data processing via Map-Reduce	Yes, but additional extensive coding is required
No, Avro was specifically designed for data storage only	Avro specifies metadata that allows easier data access. This data cannot be used as part of mapreduce execution, rather input specification only.

18. What is distributed cache?
The distributed cache is special component on namenode that will cache frequently used data for faster client response. It is used during reduce step.	The distributed cache is special component on datanode that will cache frequently used data for faster client response. It is used during map step.
The distributed cache is a component that caches java objects.	The distributed cache is a component that allows developers to deploy jars for Map-Reduce processing.

19. What is the best performance one can expect from a Hadoop cluster?
The best performance expectation one can have is measured in seconds. This is because Hadoop can only be used for batch processing	The best performance expectation one can have is measured in milliseconds. This is because Hadoop executes in parallel across so many machines
The best performance expectation one can have is measured in minutes. This is because Hadoop can only be used for batch processing	It depends on on the design of the map-reduce program, how many machines in the cluster, and the amount of data being retrieved

20. What is writable?
Writable is a java interface that needs to be implemented for streaming data to remote servers.	Writable is a java interface that needs to be implemented for HDFS writes.
Writable is a java interface that needs to be implemented for MapReduce processing.	None of these answers are correct.

Free Hadoop Quiz Online Practice Test1

Thank you very much for taking test

Test your caliber

1. What is a SequenceFile?
ASequenceFilecontains a binaryencoding ofan arbitrary numberof homogeneous writable objects.	ASequenceFilecontains a binary encoding of an arbitrary number of heterogeneous writable objects.
ASequenceFilecontains a binary encoding of an arbitrary number of WritableComparable objects, in sorted order.	ASequenceFilecontains a binary encoding of an arbitrary number key-value pairs. Each key must be the same type. Each value must be sametype.

2. Is there a map input format?
Yes, but only in Hadoop 0.22+.	Yes, there is a special format for map files.
No, but sequence file input format can read map files.	Both 2 and 3 are correct answers.

3. In a MapReduce job, you want each of you input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?
Increase the parameter that controls minimum split size in the job configuration.	Write a custom MapRunner that iterates over all key-value pairs in the entire file.
Set the number of mappers equal to the number of input files you want to process.	Write a custom FileInputFormat and override the method isSplittable to always return false.

4. Which of the following best describes the workings of TextInputFormat?
Input file splits may cross line breaks. A line thatcrosses tile splits is ignored.	The input file is split exactly at the line breaks, so each Record Reader will read a series of complete lines.
Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of both splits containing the brokenline.	Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the end of the brokenline.

5. Which of the following statements most accurately describes the relationship between MapReduce and Pig?
Pig provides additional capabilities that allow certain types of data manipulation not possible with MapReduce.	Pig provides no additional capabilities to MapReduce. Pig programs are executed as MapReduce jobs via the Pig interpreter.
Pig programs rely on MapReduce but are extensible, allowing developers to do special-purpose processing not provided by MapReduce	Pig provides the additional capability of allowing you to control the flow of multiple MapReduce jobs.

6. You need to import a portion of a relational database every day as files to HDFS, and generate Java classes to Interact with your imported data. Which of the following tools should you use to accomplish this?
Pig	Hue
Hive	Flume

7. You have an employee who is a Date Analyst and is very comfortable with SQL. He would like to run ad-hoc analysis on data in your HDFS duster. Which of the following is a data warehousing software built on top of Apache Hadoop that defines a simple SQL-like query language well-suited for this kind of user?
Pig	Hue
Hive	Sqoop

8. Workflows expressed in Oozie can contain:
Iterative repetition of MapReduce jobs until a desired answer or state is reached.	Sequences of MapReduce and Pig jobs. These are limited to linear sequences of actions with exception handlers but no forks.
Sequences of MapReduce jobs only; no Pig or Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins.	Sequences of MapReduce and Pig. These sequences can be combined with other actions including forks, decision points, and path joins.

9. You need a distributed, scalable, data Store that allows you random, realtime read/write access to hundreds of terabytes of data. Which of the following would you use?
Hue	Pig
Hive	HBase

10. Which of the following utilities allows you to create and run MapReduce jobs with any executable or script as the mapper and/or the reducer?
Oozie	Sqoop
Flume	Hadoop Streaming

11. You are running a Hadoop cluster with all monitoring facilities properly configured. Which scenario will go undetected.?
Map or reduce tasks that are stuck in an infinite loop.	HDFS is almost full.
The NameNode goes down.	A DataNode is disconnectedfrom the cluster.

12. Which of the following scenarios makes HDFS unavailable?
JobTracker failure	TaskTracker failure
DataNode failure	NameNode failure

13. Which MapReduce stage serves as a barrier, where all previous stages must be completed before it may proceed?
Combine	Group (a.k.a. 'shuffle')
Reduce	Write

14. Which of the following statements most accurately describes the general approach to error recovery when using MapReduce?
Ranger	Longhorn
Lonestar	Spur

15. The Combine stage, if present, must perform the same aggregation operation as Reduce.
True	False
All of the Above	None of these

16. What is the implementation language of the Hadoop MapReduce framework?
Java	C
FORTRAN	Python

17. Which of the following MapReduce execution frameworks focus on execution in sharedmemory environments?
Hadoop	Twister
Phoenix	All of the Above

18. How can a distributed filesystem such as HDFS provide opportunities for optimization of a MapReduce operation?
Data represented in a distributed filesystem is already sorted.	Distributed filesystems must always be resident in memory, which is much faster than disk.
Data storage and processing can be co-located on the same node, so that most input data relevant to Map or Reduce will be present on local disks or cache.	A distributed filesystem makes random access faster because of the presence of a dedicated node serving file metadata.

19. What is the input to the Reduce function?
One key and a list of all values associated with that key.	One key and a list of some values associated with that key.
An arbitrarily sized list of key/value pairs.	All of the above

20. Which MapReduce phase is theoretically able to utilize features of the underlying file system in order to optimize parallel execution?
Split	Map
Combine	All of the Above

Hadoop Online Practice Quiz Tests

Hadoop Online Quiz Test1

Thank you very much for taking test

Test your caliber

1. What is a SequenceFile?
ASequenceFilecontains a binaryencoding ofan arbitrary numberof homogeneous writable objects.	ASequenceFilecontains a binary encoding of an arbitrary number of heterogeneous writable objects.
ASequenceFilecontains a binary encoding of an arbitrary number of WritableComparable objects, in sorted order.	ASequenceFilecontains a binary encoding of an arbitrary number key-value pairs. Each key must be the same type. Each value must be sametype.

2. Is there a map input format?
Yes, but only in Hadoop 0.22+.	Yes, there is a special format for map files.
No, but sequence file input format can read map files.	Both 2 and 3 are correct answers.

3. In a MapReduce job, you want each of you input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?
Increase the parameter that controls minimum split size in the job configuration.	Write a custom MapRunner that iterates over all key-value pairs in the entire file.
Set the number of mappers equal to the number of input files you want to process.	Write a custom FileInputFormat and override the method isSplittable to always return false.

4. Which of the following best describes the workings of TextInputFormat?
Input file splits may cross line breaks. A line thatcrosses tile splits is ignored.	The input file is split exactly at the line breaks, so each Record Reader will read a series of complete lines.
Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of both splits containing the brokenline.	Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the end of the brokenline.

5. Which of the following statements most accurately describes the relationship between MapReduce and Pig?
Pig provides additional capabilities that allow certain types of data manipulation not possible with MapReduce.	Pig provides no additional capabilities to MapReduce. Pig programs are executed as MapReduce jobs via the Pig interpreter.
Pig programs rely on MapReduce but are extensible, allowing developers to do special-purpose processing not provided by MapReduce	Pig provides the additional capability of allowing you to control the flow of multiple MapReduce jobs.

6. You need to import a portion of a relational database every day as files to HDFS, and generate Java classes to Interact with your imported data. Which of the following tools should you use to accomplish this?
Pig	Hue
Hive	Flume

7. You have an employee who is a Date Analyst and is very comfortable with SQL. He would like to run ad-hoc analysis on data in your HDFS duster. Which of the following is a data warehousing software built on top of Apache Hadoop that defines a simple SQL-like query language well-suited for this kind of user?
Pig	Hue
Hive	Sqoop

8. Workflows expressed in Oozie can contain:
Iterative repetition of MapReduce jobs until a desired answer or state is reached.	Sequences of MapReduce and Pig jobs. These are limited to linear sequences of actions with exception handlers but no forks.
Sequences of MapReduce jobs only; no Pig or Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins.	Sequences of MapReduce and Pig. These sequences can be combined with other actions including forks, decision points, and path joins.

9. You need a distributed, scalable, data Store that allows you random, realtime read/write access to hundreds of terabytes of data. Which of the following would you use?
Hue	Pig
Hive	HBase

10. Which of the following utilities allows you to create and run MapReduce jobs with any executable or script as the mapper and/or the reducer?
Oozie	Sqoop
Flume	Hadoop Streaming

11. You are running a Hadoop cluster with all monitoring facilities properly configured. Which scenario will go undetected.?
Map or reduce tasks that are stuck in an infinite loop.	HDFS is almost full.
The NameNode goes down.	A DataNode is disconnectedfrom the cluster.

12. Which of the following scenarios makes HDFS unavailable?
JobTracker failure	TaskTracker failure
DataNode failure	NameNode failure

13. Which MapReduce stage serves as a barrier, where all previous stages must be completed before it may proceed?
Combine	Group (a.k.a. 'shuffle')
Reduce	Write

14. Which of the following statements most accurately describes the general approach to error recovery when using MapReduce?
Ranger	Longhorn
Lonestar	Spur

15. The Combine stage, if present, must perform the same aggregation operation as Reduce.
True	False
All of the Above	None of these

16. What is the implementation language of the Hadoop MapReduce framework?
Java	C
FORTRAN	Python

17. Which of the following MapReduce execution frameworks focus on execution in sharedmemory environments?
Hadoop	Twister
Phoenix	All of the Above

18. How can a distributed filesystem such as HDFS provide opportunities for optimization of a MapReduce operation?
Data represented in a distributed filesystem is already sorted.	Distributed filesystems must always be resident in memory, which is much faster than disk.
Data storage and processing can be co-located on the same node, so that most input data relevant to Map or Reduce will be present on local disks or cache.	A distributed filesystem makes random access faster because of the presence of a dedicated node serving file metadata.

19. What is the input to the Reduce function?
One key and a list of all values associated with that key.	One key and a list of some values associated with that key.
An arbitrarily sized list of key/value pairs.	All of the above

20. Which MapReduce phase is theoretically able to utilize features of the underlying file system in order to optimize parallel execution?
Split	Map
Combine	All of the Above

Hadoop Online Quiz Test2

Which of the following utilities allows you to create and run Map Reduce jobs with any executable or script as the mapper and/or the reducer?

A. Oozie
B. Sqoop
C. Flume
D. Hadoop Str

Answer: D

You need a distributed, scalable, data Store that allows you random, realtime read/write access to hundreds of terabytes of data. Which of the following would you use?

A. Hue
B. Pig
C. Hive
D. Oozie
E. HBase
F. Flume
G. Sqoop

Answer: E

Workflows expressed in Oozie can contain:

A. Iterative repetition of MapReduce jobs until a desired answer or state is reached.
B. Sequences of MapReduce and Pig jobs. These are limited to linear sequences of actions with exception handlers but no forks.
C. Sequences of MapReduce jobs only; no Pig or Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins.
D. Sequences of MapReduce and Pig. These sequences can be combined with other actions including forks, decision points, and path joins.

Answer: D

You have an employee who is a Date Analyst and is very comfortable with SQL. He would like to run ad-hoc analysis on data in your HDFS duster. Which of the following is a data warehousing software built on top of Apache Hadoop that defines a simple SQL-like query language well-suited for this kind of user?

A. Pig
B. Hue
C. Hive
D. Sqoop
E. Oozie
F. Flume
G. Hadoop Streaming

Answer: C