Cloudera CCD-333 : Cloudera Certified Developer for Apache Hadoop

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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. When is the reduce method first called in a MapReduce job?

A) Reducers start copying intermediate key value pairs from each Mapper as soon as it has completed. The reduce method is called as soon as the intermediate key-value pairs start to arrive.
B) Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The programmer can configure in the job what percentage of the intermediate data should arrive before the reduce method begins.
C) Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The reduce method is called only after all intermediate data has been copied and sorted.
D) Reduce methods and map methods all start at the beginning of a job, in order to provide optimal performance for map-only or reduce-only jobs.

2. Combiners Increase the efficiency of a MapReduce program because:

A) They provide an optimization and reduce the total number of computations that are needed to execute an algorithm by a factor of n, where is the number of reducer.
B) They aggregate intermediate map output locally on each individual machine and therefore reduce the amount of data that needs to be shuffled across the network to the reducers.
C) They aggregate intermediate map output horn a small number of nearby (i.e., rack-local) machines and therefore reduce the amount of data that needs to be shuffled across the network to the reducers.
D) They provide a mechanism for different mappers to communicate with each Other, thereby reducing synchronization overhead.

3. What is the behavior of the default partitioner?

A) The default partitioner assigns key value pairs to reducers based on an internal random number generator.
B) The default partitioner computes the hash of the key. Hash values between specific ranges are associated with different buckets, and each bucket is assigned to a specific reducer.
C) The default partitioner computes the hash of the value and takes the mod of that value with the number of reducers. The result determines the reducer assigned to process the key value pair.
D) The default partitioner computes the hash of the key and divides that value modulo the number of reducers. The result determines the reducer assigned to process the key-value pair.
E) The default partitioner implements a round robin strategy, shuffling the key value pairs to each reducer in turn. This ensures an even partition of the key space.

4. Workflows expressed in Oozie can contain:

A) Sequences of MapReduce and Pig jobs. These are limited to linear sequences of actions with exception handlers but no forks.
B) Iterative repetition of MapReduce jobs until a desired answer or state is reached.
C) Sequences of MapReduce and Pig. These sequences can be combined with other actions including forks, decision points, and path joins.
D) Sequences of MapReduce jobs only; no Pig or Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins.

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

Solutions: