Apache-Hadoop-Developer最も有効な質問と解答で勉強

For each intermediate key, each reducer task can emit:

• A. As many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs.
• B. One final key-value pair per value associated with the key; no restrictions on the type.
• 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 key; no restrictions on the type.
• E. 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).

What does the following command do?
register '/piggyban):/pig-files.jar';

• A. Invokes the user-defined functions contained in the jar file
• B. Transforms Pig user-defined functions into a format that Hive can accept
• C. Assigns a name to a user-defined function or streaming command
• D. Specifies the location of the JAR file containing the user-defined functions

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. 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.
• B. 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.
• C. 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.
• D. There is no difference in output between the two settings.

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?

• A. Disk I/O and network I/O
• B. Processor and disk I/O
• C. Processor and RAM
• D. Processor and network I/O

Workflows expressed in Oozie can contain:

• A. Sequences of MapReduce job only; on Pig on Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins.
• 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 and Pig. These sequences can be combined with other actions including forks, decision points, and path joins.
• D. Iterntive repetition of MapReduce jobs until a desired answer or state is reached.