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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Hadoop Fundamentals & Architecture | 20% | - HDFS operations and file management - YARN architecture and job execution - MapReduce concepts and job lifecycle |
| Topic 2: Data Ingestion | 25% | - Import/export data using Sqoop - Ingest streaming data with Flume - Load data into HDFS from external sources |
| Topic 3: Apache Pig Development | 30% | - Debug and tune Pig jobs - Data transformation, filtering, joining, and aggregation - Write and optimize Pig Latin scripts |
| Topic 4: Apache Hive Development | 25% | - Write and optimize HiveQL queries - Create and manage Hive tables, partitions, and buckets - Use Hive functions, views, and metastore |
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 key; no restrictions on the type.
B) 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).
C) As many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs.
D) One final key-value pair per value associated with the key; no restrictions on the type.
E) 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.
2. You have just executed a MapReduce job. Where is intermediate data written to after being emitted from the Mapper's map method?
A) Into in-memory buffers that spill over to the local file system of the TaskTracker node running the Mapper.
B) Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node running the Reducer
C) Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into HDFS.
D) Intermediate data in streamed across the network from Mapper to the Reduce and is never written to disk.
E) Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into HDFS.
3. Examine the following Hive statements:
Assuming the statements above execute successfully, which one of the following statements is true?
A) Each reducer generates a file sorted by age
B) The output of each reducer is only the age column
C) The output is guaranteed to be a single file with all the data sorted by age
D) The SORT BY command causes only one reducer to be used
4. You need to move a file titled "weblogs" into HDFS. When you try to copy the file, you can't. You know you have ample space on your DataNodes. Which action should you take to relieve this situation and store more files in HDFS?
A) Decrease the block size on your remaining files.
B) Increase the block size on your remaining files.
C) Increase the block size on all current files in HDFS.
D) Decrease the block size on all current files in HDFS.
E) Increase the number of disks (or size) for the NameNode.
F) Increase the amount of memory for the NameNode.
5. Which HDFS command uploads a local file X into an existing HDFS directory Y?
A) hadoop fs -get X Y
B) hadoop fs -localPut X Y
C) hadoop scp X Y
D) hadoop fs-put X Y
Solutions:
| Question # 1 Answer: E | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: D |





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