Learn with Hortonworks : HADOOP-PR000007 training material for 100% pass

Which best describes how TextInputFormat processes input files and line breaks?

• A. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the
  split that contains the beginning of the broken line.
• B. Input file splits may cross line breaks. A line that crosses file splits is ignored.
• C. The input file is split exactly at the line breaks, so each RecordReader will read a series of complete
  lines.
• D. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the
  split that contains the end of the broken line.
• E. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of
  both splits containing the broken line.

MapReduce v2 (MRv2/YARN) is designed to address which two issues?

• A. Reduce complexity of the MapReduce APIs.
• B. Ability to run frameworks other than MapReduce, such as MPI.
• C. Single point of failure in the NameNode.
• D. HDFS latency.
• E. Resource pressure on the JobTracker.
• F. Standardize on a single MapReduce API.

You want to count the number of occurrences for each unique word in the supplied input data. You've
decided to implement this by having your mapper tokenize each word and emit a literal value 1, and then
have your reducer increment a counter for each literal 1 it receives. After successful implementing this, it
occurs to you that you could optimize this by specifying a combiner. Will you be able to reuse your
existing Reduces as your combiner in this case and why or why not?

• A. No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
• B. No, because the Combiner is incompatible with a mapper which doesn't use the same data type for
  both the key and value.
• C. Yes, because the sum operation is both associative and commutative and the input and output types to
  the reduce method match.
• D. No, because the Reducer and Combiner are separate interfaces.
• E. Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as
  a combiner.

Determine which best describes when the reduce method is first called in a MapReduce job?

• 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. 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.
• C. 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.
• D. 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.

You are developing a MapReduce job for sales reporting. The mapper will process input keys
representing the year (IntWritable) and input values representing product indentifies (Text).
Indentify what determines the data types used by the Mapper for a given job.

• A. The mapper-specification.xml file submitted with the job determine the mapper's input key and value
  types.
• B. The data types specified in HADOOP_MAP_DATATYPES environment variable
• C. The InputFormat used by the job determines the mapper's input key and value types.
• D. The key and value types specified in the JobConf.setMapInputKeyClass and
  JobConf.setMapInputValuesClass methods