Online Access Free CCD-333 Exam Questions

You've written a MapReduce job that will process 500 million input records and generate 500 million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reducers which is a potential bottleneck. A custom implementation of which of the following interfaces is most likely to reduce the amount of intermediate data transferred across the network?

• A.OutputFormat
• B.InputFormat
• C.Partitioner
• D.Combiner
• E.WritableComparable
• F.Writable

MapReduce is well-suited for all of the following applications EXCEPT? (Choose one):

• A.Graph mining on a large social network (e.g., Facebook friends network).
• B.Analysis of large amounts of Web logs (queries, clicks, etc.).
• C.Online transaction processing (OLTP) for an e-commerce Website.
• D.Text mining on a large collections of unstructured documents.

If you run the word count MapReduce program with m mappers and r reducers, how many output files will you get at the end of the job? And how many key-value pairs will there be in each file? Assume k is the number of unique words in the input files.

• A.There will be r files, each with approximately k/r key-value pairs.
• B.There will be m files, each with exactly k/m key value pairs.
• C.There will be m files, each with approximately k/m key-value pairs.
• D.There will be r files, each with approximately k/m key-value pairs.
• E.There will be r files, each with exactly k/r key-value pairs.

You have an employee who is a Date Analyst and is very comfortable with SQL. He would like to run ad-hoc analysis on data in your HDFS duster. Which of the following is a data warehousing software built on top of Apache Hadoop that defines a simple SQL-like query language well-suited for this kind of user?

• A.Oozie
• B.Sqoop
• C.Pig
• D.Hue
• E.Hadoop Streaming
• F.Hive
• G.Flume

What happens in a MapReduce job when you set the number of reducers to one?

• A.A single reducer gathers and processes all the output from all the mappers. The output is written in as many separate files as there are mappers.
• B.Setting the number of reducers to one is invalid, and an exception is thrown.
• C.Setting the number of reducers to one creates a processing bottleneck, and since the number of reducers as specified by the programmer is used as a reference value only, the MapReduce runtime provides a default setting for the number of reducers.
• D.A single reducer gathers and processes all the output from all the mappers. The output is written to a single file in HDFS.