41. Will settings using Java API overwrite values in configuration files?
A. No. The configuration settings in the configuration file takes precedence
B. Yes. The configuration settings using Java API take precedence
C. It depends when the developer reads the configuration file. If it is read first then no.
D. 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.
Answer: B
42. What is AVRO?
A. Avro is a java serialization library
B. Avro is a java compression library
C. Avro is a java library that create splittable files
D. None of these answers are correct
Answer: A
43. Can you run Map - Reduce jobs directly on Avro data?
A. Yes, Avro was specifically designed for data processing via Map-Reduce
B. Yes, but additional extensive coding is required
C. No, Avro was specifically designed for data storage only
D. Avro specifies metadata that allows easier data access. This data cannot be used as part of mapreduce execution, rather input specification only.
Answer: A
44. What is distributed cache?
A. The distributed cache is special component on namenode that will cache frequently used data for faster client response. It is used during reduce step.
B. The distributed cache is special component on datanode that will cache frequently used data for faster client response. It is used during map step.
C. The distributed cache is a component that caches java objects.
D. The distributed cache is a component that allows developers to deploy jars for Map-Reduce processing.
Answer: D
45. What is the best performance one can expect from a Hadoop cluster?
A. The best performance expectation one can have is measured in seconds. This is because Hadoop can only be used for batch processing
B. The best performance expectation one can have is measured in milliseconds. This is because Hadoop executes in parallel across so many machines
C. The best performance expectation one can have is measured in minutes. This is because Hadoop can only be used for batch processing
D. It depends on on the design of the map-reduce program, how many machines in the cluster, and the amount of data being retrieved
Answer: A
46. What is writable?
A. Writable is a java interface that needs to be implemented for streaming data to remote servers.
B. Writable is a java interface that needs to be implemented for HDFS writes.
C. Writable is a java interface that needs to be implemented for MapReduce processing.
D. None of these answers are correct.
Answer: C
47. The Hadoop API uses basic Java types such as LongWritable, Text, IntWritable. They have almost the same features as default java classes. What are these writable data types optimized for?
A. Writable data types are specifically optimized for network transmissions
B. Writable data types are specifically optimized for file system storage
C. Writable data types are specifically optimized for map-reduce processing
D. Writable data types are specifically optimized for data retrieval
Answer: A
48. Can a custom type for data Map-Reduce processing be implemented?
A. No, Hadoop does not provide techniques for custom datatypes.
B. Yes, but only for mappers.
C. Yes, custom data types can be implemented as long as they implement writable interface.
D. Yes, but only for reducers.
Answer: C
49. What happens if mapper output does not match reducer input?
A. No, Hadoop does not provide techniques for custom datatypes.
B. Yes, but only for mappers.
C. Yes, custom data types can be implemented as long as they implement writable interface.
D. Yes, but only for reducers.
Answer: C
50. Can you provide multiple input paths to a map-reduce jobs?
A. Yes, but only in Hadoop 0.22+.
B. No, Hadoop always operates on one input directory.
C. Yes, developers can add any number of input paths.
D. Yes, but the limit is currently capped at 10 input paths.
Answer: C
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A. No. The configuration settings in the configuration file takes precedence
B. Yes. The configuration settings using Java API take precedence
C. It depends when the developer reads the configuration file. If it is read first then no.
D. 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.
Answer: B
42. What is AVRO?
A. Avro is a java serialization library
B. Avro is a java compression library
C. Avro is a java library that create splittable files
D. None of these answers are correct
Answer: A
43. Can you run Map - Reduce jobs directly on Avro data?
A. Yes, Avro was specifically designed for data processing via Map-Reduce
B. Yes, but additional extensive coding is required
C. No, Avro was specifically designed for data storage only
D. Avro specifies metadata that allows easier data access. This data cannot be used as part of mapreduce execution, rather input specification only.
Answer: A
44. What is distributed cache?
A. The distributed cache is special component on namenode that will cache frequently used data for faster client response. It is used during reduce step.
B. The distributed cache is special component on datanode that will cache frequently used data for faster client response. It is used during map step.
C. The distributed cache is a component that caches java objects.
D. The distributed cache is a component that allows developers to deploy jars for Map-Reduce processing.
Answer: D
45. What is the best performance one can expect from a Hadoop cluster?
A. The best performance expectation one can have is measured in seconds. This is because Hadoop can only be used for batch processing
B. The best performance expectation one can have is measured in milliseconds. This is because Hadoop executes in parallel across so many machines
C. The best performance expectation one can have is measured in minutes. This is because Hadoop can only be used for batch processing
D. It depends on on the design of the map-reduce program, how many machines in the cluster, and the amount of data being retrieved
Answer: A
46. What is writable?
A. Writable is a java interface that needs to be implemented for streaming data to remote servers.
B. Writable is a java interface that needs to be implemented for HDFS writes.
C. Writable is a java interface that needs to be implemented for MapReduce processing.
D. None of these answers are correct.
Answer: C
47. The Hadoop API uses basic Java types such as LongWritable, Text, IntWritable. They have almost the same features as default java classes. What are these writable data types optimized for?
A. Writable data types are specifically optimized for network transmissions
B. Writable data types are specifically optimized for file system storage
C. Writable data types are specifically optimized for map-reduce processing
D. Writable data types are specifically optimized for data retrieval
Answer: A
48. Can a custom type for data Map-Reduce processing be implemented?
A. No, Hadoop does not provide techniques for custom datatypes.
B. Yes, but only for mappers.
C. Yes, custom data types can be implemented as long as they implement writable interface.
D. Yes, but only for reducers.
Answer: C
49. What happens if mapper output does not match reducer input?
A. No, Hadoop does not provide techniques for custom datatypes.
B. Yes, but only for mappers.
C. Yes, custom data types can be implemented as long as they implement writable interface.
D. Yes, but only for reducers.
Answer: C
50. Can you provide multiple input paths to a map-reduce jobs?
A. Yes, but only in Hadoop 0.22+.
B. No, Hadoop always operates on one input directory.
C. Yes, developers can add any number of input paths.
D. Yes, but the limit is currently capped at 10 input paths.
Answer: C
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