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Cloudera CCD-333 Exam Syllabus Topics:

Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. What types of algorithms are difficult to express MapReduce?

A) Relational operations on large amounts of structured and semi structured data.
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) Algorithms that require applying the same mathematical function to large numbers of individual binary records.
E) Algorithms that requite global, shared state.

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

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

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) 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, 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.
C) 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.
D) There is no difference in output between the two settings.

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

5. For each intermediate key, each reducer task can emit:

A) As many final key-value pairs as desired, as long as all the keys have the same type and all the values have the same type.
B) As many final key value pairs as desired. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous)
C) One final key-value pair per value associated with the key; no restrictions on the type.
D) As many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs.
E) One final key value pair per key; no restrictions on the type.

Solutions: