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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. For each intermediate key, each reducer task can emit:

A) One final key-value pair per value associated with the key; no restrictions on the type.
B) As many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs.
C) One final key-value pair per key; no restrictions on the type.
D) As many final key-value pairs as desired, as long as all the keys have the same type and all the values have the same type.
E) As many final key-value pairs as desired. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous).

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

3. You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat: the mapper applies a regular expression over input values and emits key-values pairs with the key consisting of the matching text, and the value containing the filename and byte offset. Determine the difference between setting the number of reduces to one and settings the number of reducers to zero.

A) With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one reducer, all instances of matching patterns are gathered together in one file on HDFS.
B) With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances of matching patterns are stored in a single file on HDFS.
C) There is no difference in output between the two settings.
D) With zero reducers, all instances of matching patterns are gathered together in one file on HDFS. With one reducer, instances of matching patterns are stored in multiple files on HDFS.

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

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

Solutions: