CCD-333最も有効な質問と解答で勉強

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Cloudera CCD-333 試験シラバストピック:

Cloudera Certified Developer for Apache Hadoop 認定 CCD-333 試験問題：

問題 #1

You write a MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat and the IdentityReducer: the mapper applies a regular expression over input values and emits key-value 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 reducers to zero.

A. 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.
B. With zero reducers, all instances of matching patterns are gathered together in one file on HDFS. With one reducer, instances of matching patterns stored in multiple files 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.

問題 #2

For each input key-value pair, mappers can emit:

A. As many intermediate key-value pairs as desired, but they cannot be of the same type as the input key-value pair.
B. As many intermediate key-value pairs as desired. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous).
C. As many intermediate 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 intermediate key value pair, but of the same type.
E. One intermediate key value pair, of a different type.

問題 #3

The NameNode uses RAM for the following purpose:

A. To manage distributed read and write locks on files in HDFS.
B. To store the edits log that keeps track of changes in HDFS.
C. To store filenames, list of blocks and other meta information.
D. To store the contents of files in HDFS.

問題 #4

You've written a MapReduce job that will process 500 million input records and generate 500 million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reducers which is a potential bottleneck. A custom implementation of which of the following interfaces is most likely to reduce the amount of intermediate data transferred across the network?

A. WritableComparable
B. OutputFormat
C. InputFormat
D. Writable
E. Partitioner
F. Combiner

問題 #5

For each intermediate key, each reducer task can emit:

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

解説：