CCD-333 無料問題集「Cloudera Certified Developer for Apache Hadoop」

（A）To store filenames, list of blocks and other meta information.

（B）To store the contents of files in HDFS.

（C）To store the edits log that keeps track of changes in HDFS.

（D）To manage distributed read and write locks on files in HDFS.

（A）The default partitioner assigns key value pairs to reducers based on an internal random number generator.

（B）The default partitioner implements a round robin strategy, shuffling the key value pairs to each reducer in turn. This ensures an even partition of the key space.

（C）The default partitioner computes the hash of the key. Hash values between specific ranges are associated with different buckets, and each bucket is assigned to a specific reducer.

（D）The default partitioner computes the hash of the key and divides that value modulo the number of reducers. The result determines the reducer assigned to process the key-value pair.

（E）The default partitioner computes the hash of the value and takes the mod of that value with the number of reducers. The result determines the reducer assigned to process the key value pair.

（A）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.

（B）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.

（C）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.

（D）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.

（A）The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.

（B）The values are in sorted order.

（C）Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values.

（D）The values are arbitrarily ordered, but multiple runs of the same MapReduce job will always have the same ordering.

（A）Flume

（B）Hadoop Streaming

（C）Oozie

（D）Pig

（E）Hive

（F）Hue

（G）Sqoop

（A）Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run.

（B）Because combiners perform local aggregation of word counts, thereby allowing the mappers to process input data faster.

（C）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.

（D）Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.