Online Access Free CCD-333 Exam Questions

Which of the following describes how a client reads a file from HDFS?

• A.The client queries all DataNodes in parallel. The DataNode that contains the requested data responds directly to the client. The client reads the data directly off the DataNode.
• B.The client queries the NameNode for the block location(s). The NameNode returns the block location(s) to the client. The client reads the data directly off the DataNode(s).
• C.The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode redirects the client to the DataNode that holds the requested data block(s). The client then reads the data directly off the DataNode.
• D.The client contacts the NameNode for the block location(s). The NameNode contacts theDataNode that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the client.

What types of algorithms are difficult to express MapReduce?

• A.Algorithms that require applying the same mathematical function to large numbers of individual binary records.
• B.Large-scale graph algorithms that require one-step link traversal.
• C.Text analysis algorithms on large collections of unstructured text (e.g., Web crawls).
• D.Relational operations on large amounts of structured and semi structured data.
• E.Algorithms that requite global, shared state.

You need to create a job that does frequency analysis on input data. You will do this by writing a Mapper that uses TextInputForma and splits each value (a line of text from an input file) into individual characters. For each one of these characters, you will emit the character as a key and as IntWritable as the value. Since this will produce proportionally more intermediate data than input data, which resources could you expect to be likely bottlenecks?

• A.Disk I/O and network I/O
• B.Processor and network I/O
• C.Processor and RAM
• D.Processor and disk I/O

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

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

• A.Setting the number of reducers to one is invalid, and an exception is thrown.
• B.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.
• C.A single reducer gathers and processes all the output from all the mappers. The output is written to a single file in HDFS.
• D.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.