A combiner reduces:
A. The number of values across different keys in the iterator supplied to a single reduce method call.
B. The amount of intermediate data that must be transferred between the mapper and reducer.
C. The number of input files a mapper must process.
D. The number of output files a reducer must produce.
Answer: B
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
Friday, 28 December 2018
Sunday, 25 February 2018
Cloudera CCD-410 Question Answer
Which project gives you a distributed, Scalable, data store that allows you random, realtime read/write access to hundreds of terabytes of data?
A. HBase
B. Hue
C. Pig
D. Hive
E. Oozie
F. Flume
G. Sqoop
Answer: A
You use the hadoop fs put command to write a 300 MB file using and HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this life?
A. They would see Hadoop throw an ConcurrentFileAccessException when they try to access this file.
B. They would see the current state of the file, up to the last bit written by the command.
C. They would see the current of the file through the last completed block.
D. They would see no content until the whole file written and closed.
Answer: C
A. HBase
B. Hue
C. Pig
D. Hive
E. Oozie
F. Flume
G. Sqoop
Answer: A
You use the hadoop fs put command to write a 300 MB file using and HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this life?
A. They would see Hadoop throw an ConcurrentFileAccessException when they try to access this file.
B. They would see the current state of the file, up to the last bit written by the command.
C. They would see the current of the file through the last completed block.
D. They would see no content until the whole file written and closed.
Answer: C
Tuesday, 26 December 2017
Cloudera CCD-410 Question Answer
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?
A. Yes, because the sum operation is both associative and commutative and the input and output types to the reduce method match.
B. No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
C. No, because the Reducer and Combiner are separate interfaces.
D. No, because the Combiner is incompatible with a mapper which doesn't use the same data type for both the key and value.
E. Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as a combiner.
Answer: A
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.
A. TaskTracker
B. NameNode
C. DataNode
D. JobTracker
E. Secondary NameNode
Answer: D
A. Yes, because the sum operation is both associative and commutative and the input and output types to the reduce method match.
B. No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
C. No, because the Reducer and Combiner are separate interfaces.
D. No, because the Combiner is incompatible with a mapper which doesn't use the same data type for both the key and value.
E. Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as a combiner.
Answer: A
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.
A. TaskTracker
B. NameNode
C. DataNode
D. JobTracker
E. Secondary NameNode
Answer: D
Thursday, 9 November 2017
Cloudera CCD-410 Question Answer
Determine which best describes when the reduce method is first called in a MapReduce job?
A. 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.
B. 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.
C. 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.
D. 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.
Answer: B
A. 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.
B. 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.
C. 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.
D. 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.
Answer: B
Wednesday, 13 September 2017
Cloudera CCD-410 Question Answer
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?
A. Increase the parameter that controls minimum split size in the job configuration.
B. Write a custom MapRunner that iterates over all key-value pairs in the entire file.
C. Set the number of mappers equal to the number of input files you want to process.
D. Write a custom FileInputFormat and override the method isSplitable to always return false.
Answer: D
Which process describes the lifecycle of a Mapper?
A. The JobTracker calls the TaskTracker's configure () method, then its map () method and finally its close () method.
B. The TaskTracker spawns a new Mapper to process all records in a single input split.
C. The TaskTracker spawns a new Mapper to process each key-value pair.
D. The JobTracker spawns a new Mapper to process all records in a single file.
Answer: B
A. Increase the parameter that controls minimum split size in the job configuration.
B. Write a custom MapRunner that iterates over all key-value pairs in the entire file.
C. Set the number of mappers equal to the number of input files you want to process.
D. Write a custom FileInputFormat and override the method isSplitable to always return false.
Answer: D
Which process describes the lifecycle of a Mapper?
A. The JobTracker calls the TaskTracker's configure () method, then its map () method and finally its close () method.
B. The TaskTracker spawns a new Mapper to process all records in a single input split.
C. The TaskTracker spawns a new Mapper to process each key-value pair.
D. The JobTracker spawns a new Mapper to process all records in a single file.
Answer: B
Friday, 2 September 2016
Pass4sure CCD-410 Question Answer
Which two of the following are valid statements? (Select two)
A. HDFS is optimized for storing a large number of files smaller than the HDFS block size.
B. HDFS has the Characteristic of supporting a "write once, read many" data access model.
C. HDFS is a distributed file system that replaces ext3 or ext4 on Linux nodes in a Hadoop cluster.
D. HDFS is a distributed file system that runs on top of native OS filesystems and is well suited to storage of very large data sets.
Answer: B, D
You need to create a GUI application to help your company's sales people add and edit customer
information. Would HDFS be appropriate for this customer information file?
A. Yes, because HDFS is optimized for random access writes.
B. Yes, because HDFS is optimized for fast retrieval of relatively small amounts of data.
C. No, because HDFS can only be accessed by MapReduce applications.
D. No, because HDFS is optimized for write-once, streaming access for relatively large files.
Answer: D
CCD-410 Exam Questions CCD-333 BrainDumps Cloudera Exam Questions Dumps
A. HDFS is optimized for storing a large number of files smaller than the HDFS block size.
B. HDFS has the Characteristic of supporting a "write once, read many" data access model.
C. HDFS is a distributed file system that replaces ext3 or ext4 on Linux nodes in a Hadoop cluster.
D. HDFS is a distributed file system that runs on top of native OS filesystems and is well suited to storage of very large data sets.
Answer: B, D
You need to create a GUI application to help your company's sales people add and edit customer
information. Would HDFS be appropriate for this customer information file?
A. Yes, because HDFS is optimized for random access writes.
B. Yes, because HDFS is optimized for fast retrieval of relatively small amounts of data.
C. No, because HDFS can only be accessed by MapReduce applications.
D. No, because HDFS is optimized for write-once, streaming access for relatively large files.
Answer: D
CCD-410 Exam Questions CCD-333 BrainDumps Cloudera Exam Questions Dumps
Thursday, 9 June 2016
Pass4sure CCD-410 Question Answer
In the standard word count MapReduce algorithm, why might using a combiner reduce the overall Job running time?
A. Because combiners perform local aggregation of word counts, thereby allowing the mappers to process input data faster.
B. Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run.
C. Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.
D. Because combiners perform local aggregation of word counts, thereby reducing the number of key-value pairs that need to be snuff let across the network to the reducers.
Answer: A
A. Because combiners perform local aggregation of word counts, thereby allowing the mappers to process input data faster.
B. Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run.
C. Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.
D. Because combiners perform local aggregation of word counts, thereby reducing the number of key-value pairs that need to be snuff let across the network to the reducers.
Answer: A
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