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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Ingestion | - Import data into Hadoop
|
| Data Transformation with Pig | - Pig Latin programming
|
| Data Processing | - End-to-end Hadoop workflows
|
| Data Analysis with Hive | - Hive query development
|
Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:
1. Which YARN component is responsible for monitoring the success or failure of a Container?
A) NodeManager
B) JobTracker
C) ApplicationMaster
D) ResourceManager
2. In a MapReduce job, you want each of your input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?
A) Increase the parameter that controls minimum split size in the job configuration.
B) Write a custom FileInputFormat and override the method isSplitable to always return false.
C) Set the number of mappers equal to the number of input files you want to process.
D) Write a custom MapRunner that iterates over all key-value pairs in the entire file.
3. Which one of the following statements describes the relationship between the ResourceManager and the ApplicationMaster?
A) The ApplicationMaster starts an instance of the ResourceManager within each Container
B) The ApplicationMaster requests resources from the ResourceManager
C) The ApplicationMaster starts a single instance of the ResourceManager
D) The ResourceManager monitors and restarts any failed Containers of the ApplicationMaster
4. 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 on the TaskTracker node running the Mapper that spill over and are written into HDFS.
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 that spill over to the local file system of the TaskTracker node running the Mapper.
D) Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into HDFS.
E) Intermediate data in streamed across the network from Mapper to the Reduce and is never written to disk.
5. MapReduce v2 (MRv2/YARN) splits which major functions of the JobTracker into separate
daemons? Select two.
A) Resource management
B) Managing tasks
C) Managing file system metadata
D) Heath states checks (heartbeats)
E) MapReduce metric reporting
F) Launching tasks
G) Job scheduling/monitoring
H) Job coordination between the ResourceManager and NodeManager
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: A,G |
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