Cloudera Certified Developer for Apache Hadoop - CCD-333 Exam Practice Test

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 key-value pairs that need to be snuff let across the network to the reducers. C. Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run. D. Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.

A. Yes, but only if one of the tables fits into memory. B. Yes. C. No, MapReduce cannot perform relational operations. D. No, but it can be done with either Pig or Hive. E. Yes, so long as both tables fit into memory.

A. There will be m files, each with approximately k/m key-value pairs. B. There will be m files, each with exactly k/m key value pairs. C. There will be r files, each with exactly k/r key-value pairs. D. There will be r files, each with approximately k/m key-value pairs. E. There will be r files, each with approximately k/r key-value pairs.

A. Flume B. Oozie C. Hue D. Sqoop E. Hadoop Streaming F. Hive G. Pig

A. Each slave node runs a DataNode daemon, but only a fraction of the slave nodes run TaskTrackers. B. Each slave node runs a TaskTracker and a DataNode daemon. C. Each slave node runs a TaskTracker, but only a fraction of the slave nodes run DataNode daemons. D. Each slave node either runs a TaskTracker or a DataNode daemon, but not both. E. Each slave node runs a JobTracker and a DataNode daemon.

A. No, because HDFS can only be accessed by MapReduce applications. B. Yes, because HDFS is optimized for fast retrieval of relatively small amounts of data. C. No, because HDFS is optimized for write-once, streaming access for relatively large files. D. Yes, because HDFS is optimized for random access writes.