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Cloudera Certified Developer for Apache Hadoop Sample Questions:

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

A) 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.
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 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.
D) 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.

2. In a large MapReduce job with m mappers and r reducers, how many distinct copy operations will there be in the sort/shuffle phase?

A) r
B) mxr (i.e., m multiplied by r)
C) m+r (i.e., m plus r)
D) m
E) mr (i.e., m to the power of r)

3. Which of the following best describes the workings of TextInputFormat?

A) Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of both splits containing the broken line.
B) Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the end of the broken line.
C) Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the beginning of the broken line.
D) The input file is split exactly at the line breaks, so each Record Reader will read a series of complete lines.
E) Input file splits may cross line breaks. A line that crosses tile splits is ignored.

4. Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated file in HDFS.

A) Yes.
B) No, but it can be done with either Pig or Hive.
C) No, MapReduce cannot perform relational operations.
D) Yes, but only if one of the tables fits into memory.
E) Yes, so long as both tables fit into memory.

5. You write a MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat and the IdentityReducer: the mapper applies a regular expression over input values and emits key-value pairs with the key consisting of the matching text, and the value containing the filename and byte offset. Determine the difference between setting the number of reducers to zero.

A) With zero reducers, all instances of matching patterns are gathered together in one file on HDFS. With one reducer, instances of matching patterns stored in multiple files on HDFS.
B) With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances of matching patterns are stored in a single file on HDFS.
C) There is no difference in output between the two settings.
D) With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one reducer, all instances of matching patterns are gathered together in one file on HDFS.

Solutions: