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Cloudera CCD-333 Exam Syllabus Topics:

Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. What is the difference between a failed task attempt and a killed task attempt?

A) A failed task attempt is a task attempt that threw an unhandled exception. A killed task attempt is one that was terminated by the JobTracker.
B) A failed task attempt is a task attempt that threw a RuntimeException (i.e., the task fails). A killed task attempt is a task attempt that threw any other type of exception (e.g., IOException); the execution framework catches these exceptions and reports them as killed.
C) A failed task attempt is a task attempt that did not generate any key value pairs. A killed task attempt is a task attempt that threw an exception, and thus killed by the execution framework.
D) A failed task attempt is a task attempt that completed, but with an unexpected status value. A killed task attempt is a duplicate copy of a task attempt that was started as part of speculative execution.

2. You have the following key value pairs as output from your Map task:
(The, 1)
(Fox, 1)
(Runs, 1)
(Faster, 1)
(Than, 1)
(The, 1)
(Dog, 1)
How many keys will be passed to the reducer?

A) Two
B) Three
C) Four
D) Six
E) Five
F) One

3. To process input key-value pairs, your mapper needs to load a 512 MB data file in memory. What is the best way to accomplish this?

A) Place the data file in theDistributedCacheand read the data into memory in the configure method of the mapper.
B) Place the datafile in the DataCache and read the data into memory in the configure method ofthe mapper.
C) Place the data file in theDistributedCacheand read the data into memory in the map method of the mapper.
D) Serialize the data file, insert it in the Jobconf object, and read the data into memory in the configure method of the mapper.

4. You need to create a GUI application to help your company's sales people add and edit customer information. Would HDFS be appropriate for this customer information file?

A) No, because HDFS is optimized for write-once, streaming access for relatively large files.
B) Yes, because HDFS is optimized for fast retrieval of relatively small amounts of data.
C) No, because HDFS can only be accessed by MapReduce applications.
D) Yes, because HDFS is optimized for random access writes.

5. 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) InputFormat
B) WritableComparable
C) Combiner
D) Partitioner
E) Writable
F) OutputFormat

Solutions: