Hortonworks Apache-Hadoop-Developer real exam prep : Hadoop 2.0 Certification exam for Pig and Hive Developer
- Exam Code: Apache-Hadoop-Developer
- Exam Name: Hadoop 2.0 Certification exam for Pig and Hive Developer
- Updated: Sep 17, 2026
- Q&As: 110 Questions and Answers
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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Processing | - End-to-end Hadoop workflows
|
| Data Transformation with Pig | - Pig Latin programming
|
| Data Ingestion | - Import data into Hadoop
|
| Data Analysis with Hive | - Hive query development
|
Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:
You are developing a combiner that takes as input Text keys, IntWritable values, and emits Text keys, IntWritable values. Which interface should your class implement?
- A. Mapper <Text, IntWritable, Text, IntWritable>
- B. Reducer <Text, IntWritable, Text, IntWritable>
- C. Combiner <Text, IntWritable, Text, IntWritable>
- D. Combiner <Text, Text, IntWritable, IntWritable>
- E. Reducer <Text, Text, IntWritable, IntWritable>
Correct Answer: B π³οΈ
Identify the MapReduce v2 (MRv2 / YARN) daemon responsible for launching application containers and monitoring application resource usage?
- A. ApplicationMasterService
- B. NodeManager
- C. ApplicationMaster
- D. JobTracker
- E. ResourceManager
- F. TaskTracker
Correct Answer: B π³οΈ
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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. Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as a combiner.
- B. Yes, because the sum operation is both associative and commutative and the input and output types to the reduce method match.
- C. No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
- D. No, because the Reducer and Combiner are separate interfaces.
- E. No, because the Combiner is incompatible with a mapper which doesn't use the same data type for both the key and value.
Correct Answer: B π³οΈ
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Which Two of the following statements are true about hdfs? Choose 2 answers
- A. Block sizes for all files must be the same size
- B. Blocks are replicated to multiple datanodes
- C. An HDFS file that is larger than dfs.block.size is split into blocks
- D. HDFS works best when storing a large number of relatively small files
Correct Answer: B,C π³οΈ
Which one of the following statements is true regarding a MapReduce job?
- A. The job's Partitioner shuffles and sorts all (key.value) pairs and sends the output to all reducers
- B. The Mapper must sort its output of (key.value) pairs in descending order based on value
- C. The default Hash Partitioner sends key value pairs with the same key to the same Reducer
- D. The reduce method is invoked once for each unique value
Correct Answer: A π³οΈ
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