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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. Which of the following statements most accurately describes the relationship between MapReduce and Pig?

A) Pig provides no additional capabilities to MapReduce. Pig programs are executed as MapReduce jobs via the Pig interpreter.
B) Pig provides additional capabilities that allow certain types of data manipulation not possible with MapReduce.
C) Pig programs rely on MapReduce but are extensible, allowing developers to do special-purpose processing not provided by MapReduce.
D) Pig provides the additional capability of allowing you to control the flow of multiple MapReduce jobs.

2. Workflows expressed in Oozie can contain:

A) Sequences of MapReduce and Pig jobs. These are limited to linear sequences of actions with exception handlers but no forks.
B) Sequences of MapReduce and Pig. These sequences can be combined with other actions including forks, decision points, and path joins.
C) Iterative repetition of MapReduce jobs until a desired answer or state is reached.
D) Sequences of MapReduce jobs only; no Pig or Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins.

3. In the standard word count MapReduce algorithm, why might using a combiner reduce the overall Job running time?

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.

4. Which statement best describes the data path of intermediate key-value pairs (i.e., output of the mappers)?

A) Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and are then copied to HDFS. Reducers read the intermediate data from HDFS.
B) Intermediate key-value pairs are written to HDFS. Reducers copy the intermediate data to the local disks of the machines running the reduce tasks.
C) Intermediate key-value pairs are written to HDFS. Reducers read the intermediate data from HDFS.
D) Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and then copied to the machine running the reduce tasks.

5. You use the hadoop fs -put command to write a 300 MB file using an HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this file?

A) They would see Hadoop throw an concurrentFileAccessException when they try to access this file.
B) They would see the current state of the file, up to the last bit written by the command.
C) They would see the content of the file through the last completed block.
D) They would see no content until the whole file is written and closed.

Solutions: