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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Analysis with Hive | - Hive query development
|
| Data Transformation with Pig | - Pig Latin programming
|
| Data Processing | - End-to-end Hadoop workflows
|
| Data Ingestion | - Import data into Hadoop
|
Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:
Question 1
On a cluster running MapReduce v1 (MRv1), a TaskTracker heartbeats into the JobTracker on your cluster, and alerts the JobTracker it has an open map task slot.
What determines how the JobTracker assigns each map task to a TaskTracker?
A. The amount of free disk space on the TaskTracker node.
B. The amount of RAM installed on the TaskTracker node.
C. The average system load on the TaskTracker node over the past fifteen (15) minutes.
D. The location of the InsputSplit to be processed in relation to the location of the node.
E. The number and speed of CPU cores on the TaskTracker node.
Question 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.
Question 3
In Hadoop 2.0, which one of the following statements is true about a standby NameNode?
The Standby NameNode:
A. Communicates directly with the active NameNode to maintain the state of the active NameNode.
B. Processes all client requests and block reports from the appropriate DataNodes.
C. Runs on the same machine and shares the memory of the active NameNode.
D. Receives the same block reports as the active NameNode.
Question 4
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 DistributedCache and read the data into memory in the map method of the mapper.
B. Place the data file in the DataCache and read the data into memory in the configure 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.
Question 5
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. Reducer <Text, IntWritable, Text, IntWritable>
B. Reducer <Text, Text, IntWritable, IntWritable>
C. Combiner <Text, Text, IntWritable, IntWritable>
D. Mapper <Text, IntWritable, Text, IntWritable>
E. Combiner <Text, IntWritable, Text, IntWritable>
Solutions:
| Question 1 Answer: D | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: B | Question 5 Answer: A |
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