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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. 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, thereby reducing the number of mappers that need to run.
B) Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk.
C) 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.
D) Because combiners perform local aggregation of word counts, thereby allowing the mappers to process input data faster.

2. 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 reduce method is called only after all intermediate data has been copied and sorted.
C) 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.
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.

3. Which happens if the NameNode crashes?

A) HDFS becomes unavailable until the NameNode is restored.
B) HDFS becomes temporarily unavailable until an administrator starts redirecting client requests to the Secondary NameNode.
C) The Secondary NameNode seamlessly takes over and there is no service interruption.
D) HDFS becomes unavailable to new MapReduce jobs, but running jobs will continue until completion.

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

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

5. What is the preferred way to pass a small number of configuration parameters to a mapper or reducer?

A) As key-value pairs in the jobconf object.
B) As a custom input key-value pair passed to each mapper or reducer.
C) Through a static variable in the MapReduce driver class (i.e., the class that submits the MapReduce job).
D) Using a plain text file via the Distributedcache, which each mapper or reducer reads.

Solutions: