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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Pig Development | 30% | - Write and optimize Pig Latin scripts - Data transformation, filtering, joining, and aggregation - Debug and tune Pig jobs |
| Topic 2: Apache Hive Development | 25% | - Use Hive functions, views, and metastore - Write and optimize HiveQL queries - Create and manage Hive tables, partitions, and buckets |
| Topic 3: Hadoop Fundamentals & Architecture | 20% | - MapReduce concepts and job lifecycle - YARN architecture and job execution - HDFS operations and file management |
| Topic 4: Data Ingestion | 25% | - Load data into HDFS from external sources - Ingest streaming data with Flume - Import/export data using Sqoop |
Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:
1. 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 Hadoop throw an ConcurrentFileAccessException when they try to access this file.
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 no content until the whole file written and closed.
2. Which two of the following are true about this trivial Pig program' (choose Two)
A) myfile is read from the user's home directory in HDFS
B) The contents of myfile appear on stdout
C) Pig assumes the contents of myfile are comma delimited
D) ABC has a schema associated with it
3. 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) No, because the sum operation in the reducer is incompatible with the operation of a Combiner.
C) No, because the Reducer and Combiner are separate interfaces.
D) Yes, because the sum operation is both associative and commutative and the input and output types to the reduce method match.
E) Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as a combiner.
4. 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, so long as both tables fit into memory.
B) No, but it can be done with either Pig or Hive.
C) No, MapReduce cannot perform relational operations.
D) Yes.
E) Yes, but only if one of the tables fits into memory
5. Which process describes the lifecycle of a Mapper?
A) The TaskTracker spawns a new Mapper to process each key-value pair.
B) The JobTracker spawns a new Mapper to process all records in a single file.
C) The TaskTracker spawns a new Mapper to process all records in a single input split.
D) The JobTracker calls the TaskTracker's configure () method, then its map () method and finally its close () method.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,B | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: C |
