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Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:

1. Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files in HDFS.

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

2. To process input key-value pairs, your mapper needs to lead a 512 MB data file in memory. What is the best way to accomplish this?

A) Place the data file in the DistributedCache and read the data into memory in the configure method of the mapper.
B) Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure method of the mapper.
C) Place the data file in the DistributedCache and read the data into memory in the map method of the mapper.
D) Place the data file in the DataCache and read the data into memory in the configure method of the mapper.

3. In a MapReduce job with 500 map tasks, how many map task attempts will there be?

A) Exactly 500.
B) At least 500.
C) At most 500.
D) Between 500 and 1000.
E) It depends on the number of reduces in the job.

4. You use the hadoop fs -put command to write a 300 MB file using and 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 life?

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

5. You have just executed a MapReduce job. Where is intermediate data written to after being emitted from the Mapper's map method?

A) Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into HDFS.
B) Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into HDFS.
C) Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node running the Reducer
D) Into in-memory buffers that spill over to the local file system of the TaskTracker node running the Mapper.
E) Intermediate data in streamed across the network from Mapper to the Reduce and is never written to disk.

Solutions: