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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. Your cluster has 10 DataNodes, each with a single 1 TB hard drive. You utilize all your disk capacity for HDFS, reserving none for MapReduce. You implement default replication settings. What is the storage capacity of your Hadoop cluster (assuming no compression)?

A) about 11 TB
B) about 3 TB
C) about 10 TB
D) about 5 TB

2. During the standard sort and shuffle phase of MapReduce, keys and values are passed to reducers. Which of the following is true?

A) Keys are presented to a reducer in random order; values for a given key are not sorted.
B) Keys are presented to a reducer in soiled order; values for a given key are sorted in ascending order.
C) Keys are presented to a reducer in sorted order; values for a given key are not sorted.
D) Keys are presented to a reducer in random order; values for a given key are sorted in ascending order.

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

A) As many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs.
B) 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.
C) 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)
D) One final key-value pair per value associated with the key; no restrictions on the type.
E) One final key value pair per key; no restrictions on the type.

4. You use the hadoop fs -put command to write a 300 MB file using an HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this file?

A) They would see no content until the whole file is written and closed.
B) They would see the current state of the file, up to the last bit written by the command.
C) They would see Hadoop throw an concurrentFileAccessException when they try to access this file.
D) They would see the content of the file through the last completed block.

5. In the standard word count MapReduce algorithm, why might using a combiner reduce the overall Job running time?

A) Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.
B) Because combiners perform local aggregation of word counts, thereby reducing the number of key-value pairs that need to be snuff let across the network to the reducers.
C) Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run.
D) Because combiners perform local aggregation of word counts, thereby allowing the mappers to process input data faster.

Solutions: