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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:

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

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

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

2. Which YARN component is responsible for monitoring the success or failure of a Container?

A) ApplicationMaster
B) JobTracker
C) ResourceManager
D) NodeManager

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

4. You have a directory named jobdata in HDFS that contains four files: _first.txt, second.txt, .third.txt and #data.txt. How many files will be processed by the FileInputFormat.setInputPaths () command when it's given a path object representing this directory?

A) Four, all files will be processed
B) Two, file names with a leading period or underscore are ignored
C) Three, the pound sign is an invalid character for HDFS file names
D) One, no special characters can prefix the name of an input file
E) None, the directory cannot be named jobdata

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

Solutions: