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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. In a MapReduce job with 500 map tasks, how many map task attempts will there be?

A) At most 500.
B) Exactly 500.
C) At least 500.
D) Between 500 and 1000.
E) It depends on the number of reducers in the job.

2. In the reducer, the MapReduce API provides you with an iterator over Writable values. Calling the next () method:

A) Returns a reference to a Writable object. The API leaves unspecified whether this is a reused object or a new object.
B) Returns a reference to the same writable object each time, but populated with different data.
C) Returns a reference to a different Writable object each time.
D) Returns a reference to a Writable object from an object pool.
E) Returns a reference to the same writable object if the next value is the same as the previous value, or a new writable object otherwise.

3. 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) There is no difference in output between the two settings.
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) 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.
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.

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

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

5. In a MapReduce job, you want each of you 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) Set the number of mappers equal to the number of input files you want to process.
C) Write a custom FileInputFormat and override the method isSplittable to always return false.
D) Write a custom MapRunner that iterates over all key-value pairs in the entire file.

Solutions: