Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer : Apache-Hadoop-Developer

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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:

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 no content until the whole file written and closed.
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 Hadoop throw an ConcurrentFileAccessException when they try to access this file.

2. Which two of the following statements are true about Pig's approach toward data? Choose 2 answers

A) Accepts only data that has a key/value pair structure
B) Accepts data whether it has metadata or not
C) Accepts tab-delimited text data only
D) Accepts any data: structured or unstructured
E) Accepts only data that is defined by metadata tables stored in a database

3. Which TWO of the following statements are true regarding Hive? Choose 2 answers

A) Is a relational database
B) Offers real-time queries and row level updates
C) Useful for data analysts familiar with SQL who need to do ad-hoc queries
D) Allows you to define a structure for your unstructured Big Data

4. You have a directory named jobdata in HDFS that contains four files: _first.txt, second.txt, .third.txt and #data.txt. How many files will be processed by the FileInputFormat.setInputPaths () command when it's given a path object representing this directory?

A) One, no special characters can prefix the name of an input file
B) Three, the pound sign is an invalid character for HDFS file names
C) Two, file names with a leading period or underscore are ignored
D) Four, all files will be processed
E) None, the directory cannot be named jobdata

5. What is the disadvantage of using multiple reducers with the default HashPartitioner and distributing your workload across you cluster?

A) You will longer be able to take advantage of a Combiner.
B) By using multiple reducers with the default HashPartitioner, output files may not be in globally sorted order.
C) There are no concerns with this approach. It is always advisable to use multiple reduces.
D) You will not be able to compress the intermediate data.

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